{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f5831d0-00b8-4f08-93d8-a09a28ea9000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>009</td>\n",
       "      <td>265102</td>\n",
       "      <td>25009265102</td>\n",
       "      <td>2651.02</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>16318373</td>\n",
       "      <td>134252</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>POLYGON ((-70.99091 42.73399, -70.99089 42.734...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>009</td>\n",
       "      <td>268200</td>\n",
       "      <td>25009268200</td>\n",
       "      <td>2682</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>10737361</td>\n",
       "      <td>364499</td>\n",
       "      <td>...</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>171</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>POLYGON ((-70.92052 42.79103, -70.92050 42.791...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25</td>\n",
       "      <td>009</td>\n",
       "      <td>268400</td>\n",
       "      <td>25009268400</td>\n",
       "      <td>2684</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>3065747</td>\n",
       "      <td>4715475</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>POLYGON ((-70.87547 42.79793, -70.87547 42.797...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>009</td>\n",
       "      <td>250500</td>\n",
       "      <td>25009250500</td>\n",
       "      <td>2505</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>300558</td>\n",
       "      <td>3894</td>\n",
       "      <td>...</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>POLYGON ((-71.17166 42.72127, -71.17090 42.720...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>027</td>\n",
       "      <td>731300</td>\n",
       "      <td>25027731300</td>\n",
       "      <td>7313</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>743090</td>\n",
       "      <td>7345</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>POLYGON ((-71.82361 42.24948, -71.82280 42.249...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        25        009    265102  25009265102  2651.02  Census Tract   G5020   \n",
       "1        25        009    268200  25009268200     2682  Census Tract   G5020   \n",
       "2        25        009    268400  25009268400     2684  Census Tract   G5020   \n",
       "3        25        009    250500  25009250500     2505  Census Tract   G5020   \n",
       "4        25        027    731300  25027731300     7313  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20   ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  16318373    134252  ...        0        0        0        0   \n",
       "1          S  10737361    364499  ...      171        0        0      171   \n",
       "2          S   3065747   4715475  ...       57        0        0       57   \n",
       "3          S    300558      3894  ...       53        0       53        0   \n",
       "4          S    743090      7345  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0       14        0        0       14   \n",
       "1        0       32        0        0       32   \n",
       "2        0       16        0        0       16   \n",
       "3        0       15        0        0       15   \n",
       "4        0       46       19        0       27   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-70.99091 42.73399, -70.99089 42.734...  \n",
       "1  POLYGON ((-70.92052 42.79103, -70.92050 42.791...  \n",
       "2  POLYGON ((-70.87547 42.79793, -70.87547 42.797...  \n",
       "3  POLYGON ((-71.17166 42.72127, -71.17090 42.720...  \n",
       "4  POLYGON ((-71.82361 42.24948, -71.82280 42.249...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts.  Aug'22 cleaned \"model\" version\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "#tractGeomFile = gpd.read_file(\"state_map_files/fl_pl2020_t.shp\")  #Boo!  Only has geometries.  do not use\n",
    "tractPopFile = gpd.read_file(\"state_map_files/ma_pl2020_t.dbf\")\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74433b8e-eb83-4fce-bbc5-6782ce23643e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 1620 popn tracts for MA\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "STATE = \"MA\"\n",
    "tractGeom = tractPopFile['geometry']\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #using VAP.  Could alternatively pull CVAP's from another file\n",
    "tractHisp = tractPopFile['P0040002']   #using VAP.\n",
    "tractBlack = tractPopFile['P0030004']  #using VAP.\n",
    "tractCountyNo = tractPopFile['COUNTYFP20']\n",
    "tractCountyNo = tractCountyNo.to_numpy()\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "countyNo = [0]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    countyNo[t] = int((int(tractCountyNo[t]) - 1)/2)  #US Census only uses odd county numbers (1,3,5,7,...)\n",
    "nCounties = int( np.max(countyNo)+1)\n",
    "\n",
    "isSkippedTract = [0] *nTracts  #this will house a list of tracts to be excluded from map\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "statePop = np.sum(tractPop)\n",
    "stateVAP = np.sum(tractVAP)\n",
    "\n",
    "#define handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "def plotCenter(t,geom):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t)\n",
    "    \n",
    "# ... and for printing rounded results\n",
    "def r3(input):\n",
    "    result = round(input,3)\n",
    "    return result\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])  #generic polygon for seeding lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "83a78258-9f9a-4daf-89cd-233354b46e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for MA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OPTIONAL - for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a429218-e554-4776-8aaa-2ba04e94b49c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets see if the first two tracts are simple polygons\n",
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n",
      "looking for nonPolygons in census tract data\n",
      "1073 5276 0.0330808095669997 nonPolygon tract no, pop, area\n",
      "1312 0 0.0009461697064999785 nonPolygon tract no, pop, area\n",
      "1329 0 0.08020813655750011 nonPolygon tract no, pop, area\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets see if the first two tracts are simple polygons\")\n",
    "print( type(tractGeom[0]),type(tractGeom[1]) )\n",
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    if type(tractGeom[t]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[t] = 1\n",
    "        print(t,tractPop[t], tractArea[t], \"nonPolygon tract no, pop, area\")\n",
    "        plotPoly(tractGeom[t])\n",
    "        x = tractGeom[t].centroid.x\n",
    "        y = tractGeom[t].centroid.y\n",
    "        plt.text(x+0.03,y+0.03,t,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9638d86e-1f53-4f35-9102-7e8bf4157abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#OPTIONAL - CONVEX-HULL PROBLEMATIC TRACTS.  NOT NEEDED FOR MA since these will be clipped\n",
    "for t in range(nTracts):\n",
    "    if notPoly[t] == 1:\n",
    "        dummyline = \"nothing\"\n",
    "        #tractGeom[m] = tractGeom[m].convex_hull  #uncomment this line, run this code if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b86857a9-a65c-4b36-99a2-8fcc4c3b1374",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WP_NAME</th>\n",
       "      <th>WARD</th>\n",
       "      <th>PRECINCT</th>\n",
       "      <th>DISTRICT</th>\n",
       "      <th>TOWN</th>\n",
       "      <th>TOWN_ID</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>G20USSDMAR</th>\n",
       "      <th>G20USSROCO</th>\n",
       "      <th>G20USSOAYY</th>\n",
       "      <th>G20USSOWRI</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fitchburg City Ward 2 Precinct A</td>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>2-A</td>\n",
       "      <td>FITCHBURG</td>\n",
       "      <td>97</td>\n",
       "      <td>693</td>\n",
       "      <td>338</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>712</td>\n",
       "      <td>326</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON ((-71.80725 42.58365, -71.80741 42.583...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dartmouth Town Precinct 7</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>DARTMOUTH</td>\n",
       "      <td>72</td>\n",
       "      <td>1197</td>\n",
       "      <td>1020</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1242</td>\n",
       "      <td>939</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-70.94262 41.60696, -70.94309 41.606...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dartmouth Town Precinct 8</td>\n",
       "      <td>None</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>DARTMOUTH</td>\n",
       "      <td>72</td>\n",
       "      <td>1635</td>\n",
       "      <td>937</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>1624</td>\n",
       "      <td>926</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-70.95557 41.59448, -70.95552 41.594...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dartmouth Town Precinct 9</td>\n",
       "      <td>None</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>DARTMOUTH</td>\n",
       "      <td>72</td>\n",
       "      <td>1634</td>\n",
       "      <td>1033</td>\n",
       "      <td>29</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "      <td>1618</td>\n",
       "      <td>1019</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>MULTIPOLYGON (((-70.96176 41.60028, -70.96156 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dennis Town Precinct 1</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DENNIS</td>\n",
       "      <td>75</td>\n",
       "      <td>1280</td>\n",
       "      <td>868</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1235</td>\n",
       "      <td>905</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-70.18070 41.75110, -70.18091 41.750...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            WP_NAME  WARD PRECINCT DISTRICT       TOWN  \\\n",
       "0  Fitchburg City Ward 2 Precinct A     2        A      2-A  FITCHBURG   \n",
       "1         Dartmouth Town Precinct 7  None        7        7  DARTMOUTH   \n",
       "2         Dartmouth Town Precinct 8  None        8        8  DARTMOUTH   \n",
       "3         Dartmouth Town Precinct 9  None        9        9  DARTMOUTH   \n",
       "4            Dennis Town Precinct 1  None        1        1     DENNIS   \n",
       "\n",
       "   TOWN_ID  G20PREDBID  G20PRERTRU  G20PRELJOR  G20PREGHAW  G20PREOWRI  \\\n",
       "0       97         693         338          16          10           1   \n",
       "1       72        1197        1020          22           8           5   \n",
       "2       72        1635         937          25           5          13   \n",
       "3       72        1634        1033          29          13          12   \n",
       "4       75        1280         868          20           2           8   \n",
       "\n",
       "   G20USSDMAR  G20USSROCO  G20USSOAYY  G20USSOWRI  \\\n",
       "0         712         326           0          10   \n",
       "1        1242         939          12           2   \n",
       "2        1624         926           9           0   \n",
       "3        1618        1019          18           4   \n",
       "4        1235         905          11           0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-71.80725 42.58365, -71.80741 42.583...  \n",
       "1  POLYGON ((-70.94262 41.60696, -70.94309 41.606...  \n",
       "2  POLYGON ((-70.95557 41.59448, -70.95552 41.594...  \n",
       "3  MULTIPOLYGON (((-70.96176 41.60028, -70.96156 ...  \n",
       "4  POLYGON ((-70.18070 41.75110, -70.18091 41.750...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/ma_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4a46699-25a0-48d6-b4b4-a410842aebb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2173 0.3288445045985185 3549404 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "36b56e33-3ef8-4ad1-bcf5-413a86542686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n8A1pC4DWw4OITz/FQwX5VqNeJXohRBgwHngBmA0gpVxxyi5bgSm1pPMALgdmVqax4PwFoJyBdEgqYvMoj8tnqIjmoD2J4OEd8ewaCEDZoWxyvopB665v4ZwqTUFoNIS8+F/i7riVXf+8E8v4KyjqEkFmWQb5FQUU20oot5UR6XY530aM4uqY3VwV3YendyYwLz8ftIKr9C681r8z/07LR7M2B4DpiZv50gVmll+JR+8gKo5lYC/R8FD/t3HpUHvPXaV1qG/VzVzgUaCurn63AQtrWR8FZAGfCSF6AruAB6WUJX/dUQgxC5gFEBERUc9stU75i+Mp2ZoGWkGPkE4Mm3g1xojqMU0sqSUgwBBev56XysXnybi3+H1aGs6P6CYo2QSA0EpMNoGLELiW/ozGMYh7kivQHl1Dkac3buUOvu7bngHBngAsj3G20Z/UM4TLPAaRsTUBLYKc41twKY3EpaMvxk5+LXSVSnM5a6AXQkwAMqWUu4QQV9ay/UnABiyo4/h9gAeklNuEEG8BjwFP/XVHKeU8YB44h0BowDW0KpaUYmeQB3ymdqQioZDSXRmU7slE5+OCPsQNS2Ih+iAzGqN6lt4aSIfEkliIIdwdR6mNioQCeie3Y2jSA2SHF9Amwpcg/7YE+UXi5R2M1qW61+ucg2v4MNMb3O245i0mqGQvWp5DSg8Wf/MBozTZrHL05fEIf+y/FtOVbmR0mk9+m1W0d3kGv8uuVM95LgH1iRRDgElCiHE4x9DzEELMl1JOF0LMACYAI2Xtg+YkA8lSym2Vyz/gDPRKHXT+ruj8XLFll5H7zVEANG56pM2BPGXAK/PAoLoOoVxEKpIKyfvuGLbsMoRBg7Q6QMIwujh3OAJJ7sV0HTEcq8PKQ2tnU2ot5eXLX2Zr6layP36dqE7lFLjZMNgq8KjwYebyaeiSXmeP43GuMcDJ/0tD92M8f757fNMnQocSgvqNU0H+EnHWQC+lfJzKB62VJfp/Vgb5scC/gCuklKV1pE0XQpwUQnSSUh4FRgKHGyvzrZHGoCXggV5kf3oIS2IhXpPa4TY4BICiDckU/JYAgKFNw4enVZpPud3BI8dO4qLR8FLHMLR/CajSIcn6cB+WpCIANCYdxvZe6ANM6MPcERpBCWWUfRpP2A4zd6XewmaPvVXpB330DpayQdyX5YlXsZ7Fw1L5af296IK6M8Z9I4Pb+rDe5wOGdWtLuI8J7uiOvaACR5kNnb8rkdoJzXk7lBZ2Pr/93wWMwMrKUsFWKeXdQogQ4BMp5bjK/R4AFgghDMBx4NbzyfClQGPU4X9XD2S5DY2p+oGr2+BQNEYd5fH5uHb2acEcKmcSU1zG8B1Hq5a/Ss1h3YDOdDploLHSPZlVQR7AUWqjbH82dPPFbVgo1vRSHKty2K09zm59Ap0sfozy0bGktILd5VrswhvsGqJvnsmooVfylNmVxE+m4ihKZduXT+NuNgA1BzLUehrReqp5GS5FrXaYYkVpCRaHg4h1zgHBRL4FYbHj8HfhjwGd6epWPdOZw2KnIi4fQ4Q7WR/sw5ZTfvrBHHkULX4MYfJj91U98R+5jIRdN1Ayyp/J7ScT4Vmz0YKUUlXFXMIuyWGKFaUlFNkcGDWCijIbxm3Ofg5ag4Yuw3vV2C/HlktZWBkRbr5o3AzwZ6DXCNyGhaIPNCErkrDl/gOdX0f6ZK1gxaK3mfroYILaetZ6bhXklbqoQK8ojcjXoGPP4K68FZ/GFzjHh+kVXDMwf3bwM97Y9QYAX4z9gl539+KBPx6gQjp4e8R7mLR/9mMMxFFmpnBLLhsLhqOV4BNsbs7LUVoJFegVpZH56HU82zmcZ1+qOTTBH0l/8NCah2qs+/7IL1DRluTSbA5Zg3jiWDJvdg5n7dq1JCcnM336dH4rm0GqKQLfoyOwlNkwuKiPrdIw6h2jKM2gwl5xWpAH+GZ5d75xbKaP6E/IsP5c7e+JdDhYt24dAHZ7Ka/vHE12mQ9x71yBTq8GtVMaTgV6RWkG1p253JI1ga/8lwDw04RljHp1b+VWSWznL3Ck/kTCwS846NjOkPQldHLPJLbbQj70DyfhzU9UkFfOmQr0itLEpF2S/1McNzIWt0J/XKSBdl7B7HjSl/sW7GH7iVzMaSOwpA0k+r1bsD3zLAHGIiLMBaxtPwk8fbmh36U9LIhyflSgV5QmJrQCtyEhFG1KwSj1uJvdsUqY+tFWErKdwz5l5o3k37KQiqDudDFK/F8/AEJwpVYNXKecPxXoFaUZeE1sh9fEdkzb2ZFYDx0d//07zukZJKChb/4exniPgB4D8R87BlRTSaURqUCvKE2sqOgw23dMpGOHp/HoehPXPrsSkLhGzENvTmL733ay71hXCj6ZTge3GCi7Dkyq57PSeFTPWEWpJ0eFneKNKRSuTMTz6jaUrPsc6XAQPGdOnWms1jzWb3B2Viy3SQ4XRGBxfR1fN0+2l/+XHRk7ODDjgHNnSynYylWQV86J6hmrKOdJOhzEvLWc/d7f4NERev4+i4LtsWhSd+J9099w6dTRuaPDDim7ILAbGExYrfkAJK0fj9GYTJ8Be5m9IZ59c6ZyN5/WPInB5PxTlEbWkKkEFeWSdOJANomHMlix6yPMvgnofONILD6EJnUnEqiIi63e+fd/wf9Gw37nPDwmU1t69viELIOev1X8QdzhAXw7a0TLXIhyyVIlekU5g22Lj7Nz6QkAPMwDiFu0CWkT9DnxCQACMES0qU5w5Dfnv0E9qlb5+Q3noUeHcjzlHu5q076Zcq4o1VSJXlHOoOoZls9uNL4uGFzbMTAhh6CC6tkwNeZTqluKUp3/fjIC4v6oWm3Q6YlSQV5pISrQK41qT+YessuyWzobjaY4twKJRJoLcbv6W56/+XamvPxh1Xb/hx7CGBVVneD2ldWv51+P7WkvfvpqCquPnrJeUZpZvatuhBBaYCeQIqWcIIR4FZgIWIB44FYpZX590p53rpULztHco7yz5x3WJa9DIzT0CejDqDajGBUxikBzYEtnr6a9X0NBMlz+CI7SUipOJGBvo8fhqMDNrTMajaFq154jw8lOycenzy9AKZ3kYW7w8aLDxg2g1aLz9q55bNfq5cMF/qxM64gj6xaMG5czYm6/GtsVpbk0pET/IBBzyvJKoJuUsgdwjMrpBuuZVmlFYvNiuXfVvaxLXsekdpO4q8dd5Ffk89L2lxj1wyi+O/pdS2expp/vgTUv4HjKi6N9+/H2T69y/fbNbN95HevW96mxq3+EO1Me647Z0xMtDn7sGcb9vcah8/M7PcgDmHyrXuq9wwgz5dMuZR299h3DWaOvKM2vXiV6IUQYMB54AZgNIKVcccouW4Ep9U2rtB4vb3+ZBTELkEi6+XbjuSHPoREa7ul5Dz2+dD6QTC5ObuFc/sW1H/LB2kf50NOTb7GzJfJy9ovepMtggh1pp+2u07kzZPD6+s3gZPKBp7JBq6cD0EFKbKkJYPQAV68muRxFOZv6lujnAo8Cjjq23wb8fo5pARBCzBJC7BRC7MzKyqpntpSWlFOWw/yY+fQK6MWGGzfwzYRv0AjnW0oIwaDgQYBzoo30kvSWzGpNvW7ifW8vHBpBmYeDOW+9wXNxr7DGfhVDh2ypM1m9Z3A6dXwaIdCFRqHz8zvPTCvKuTtroBdCTAAypZS76tj+JGADFjQ07amklPOklP2klP38/f3PnnOlxRm0BoLNwezJ3MOhnEM1tv0c9zNb07ZWLdsctubOXk12a43FPbfsYc5lc+i2fitZd3fgtcC7+U03kXWbhpzxMNLhIKZLV2I6R1OybXtT5lhRGk19SvRDgElCiBPAt8AIIcR8ACHEDGACcLOsfSyFOtMqFz93gzvvjXwPgLtX3V3VFPHj/R/z1KanMGgM3NvrXr66+ivC3MNaLqO//wue84NPx0JpLgCO9HJGHOxBStLXaNoe4B63N3lf3or2DD88f477mb891x0czn02LPqZa3bHcrSklom9FeUCctZAL6V8XEoZJqWMBKYBq6WU04UQY4F/AZOklKUNSdt42VdamknvbEN+deTVCCGosFVw8tMEQrP1LJ+ynHt63kOvgF4tm8ndXzLfw52xjkTkp2OIOxbLf+e9RvyOGDzzB1Mk29Kd/Zgcpaz1fb/WQ8Tnx/PUpqfof8z5ZWYLkDw9dgDbCkq4YvuRBmfJll9BRVLheV2WotTX+fSMfRcwAisr6y63SinvFkKEAJ9IKcc1RgaVC9svcb8AzmqczSmb2fHk1wS6XM+s9SY8b3GAawtnEOBfifzxv+6k6HUUDnqMbTucVS52Yz57Cx7l0bTXsRx27lp+VSD/djgwamqWgZafWA7Aqt4aukYPQzd4Ba5Ul2/sUqKtZx1+eoWV/Ne242aDsJeGNcIFKsqZNSjQSynXAmsrX9fazU9KmQqcFuRPTau0HlM7TWXtybX8Ev8Lv8T/gstgeGxpCF2CT6J1d2/p7DnpDHxy52Ec1jL0Rjf+1tXBb0v7UIiW5TtmUnhZKATaCTVreS066rQgD87r9DJ6UW4vZ3TXmdhthfTZuZCjlbU29Q3yRVs+wmPlU7wx7CceK1LPopTmoca6Uc6Ln6sfCycsJK0kjROFJ8goyaDLlC508unU0lmrQavRojW6ASCEhi7RM0mK2YPZUo5JFjNJ/yPPdJ2Kt7dHjXTFxcWcPHmS3bt3M3r0aAICAgDQ6L1487K7mJxXhKmWL4bTxPzKxwu+ZuiQsVhd/VmX/xGv3Phlo1+notRGjUevXNLK4vJIOJRBmxGhmP/yC8RqtfLCCy8AEk/PDMrKPHjssZfQ1BLY8602zFotek3tJfuERzoyXPsmQ4J2ML3zdzyWrmfLzVuqnnEoyvk603j0aqwb5ZLm2t6bLtd0Pi3IA/zZn8NszqNHz5V06FjZxv7gj/DeQIhbhUNKXk9Ip/PGg4Sv21fnecp+1TIj8Uf65e8hp/gyFkxYoIK80mxUoFeUOgQHB3PzzTcTFTWYktLO/Jh+C1FP/A4/3ApZR2D+ZJ6MTeHVE87OYBP8Pes8luekiXg4MnDE2Llu1Gt08+vWXJehKKqOXlHqIoSgQ4cOpDg8mflZdYn/18D7mJjxHnv6PsRnKc6ROp9tH8Jd4QG1Hiel3ILuuRcYb7OxMruQsUfTWd7PE5NWlbOU5qECvXJBkVKSm1pC/O5MOg4IwjPAtf5DDzQRm935HMtk0LLl8ZF4uo4H2xzeOJwMOYVMCfTm9tDqFjSb4rJZfTST3w1Wjutqfwa2u7CEod4XSKskpdVTD2OVC4J0SPasTGLPiiTKS6qHK+gxPIxhN3ZswZw5FZRa0WkFZmN12UhKiTUzE43BgM7bm+IKG/GZxVzz3iYAzGFu5HStvTrn6XYh3BtR+y8ARTkXanJw5YK3f20yW36KP219RDffWvZufp4m/Wnr7Lm5xF9xJbqQYDqsXs3lr6wht8RStX3xjb0I9HYjqdyCv1bwWVoubyRmAnCkpKzZ8q4oKtArLa4gq4yN38XWWNd3bBv6jG2DwaXl36Il1hK+PPwlAa4BdPbtTFffrgBofXwIfPJJ9KEhZL37HqMzLSw0O399jBm2jWuXPsaCcQswFZh4b/58cDh4LzSAvKwM8g/s4XWg+4gxjLnr/1rw6pRLQct/ipRL3taffqM873P05qvpMOByBk9uj6f/hTB2gtMLW1/g1+O/Vi0/MfAJbup8E0IIfG6ZTuHSpWS/+y4zgQeuHovHi6/w3u4TbMnSsuS9F3k4soJA/+FkJxxn5dH1bOqZQ4TelcsO+nJg9QpO7N+DaegolnqFcnniL3SK2EyHsEcI61HrFA+K0mAq0CstLvP4RgD6jo1gyNTuLZyb04W6h9ZYHte2eoQPKSUps/8BwNb+TyFL3Zlh0LI7aRBFRyJ5wuVvcAKmRT9Jflo4Bw4ex2T0YWXnWJKCkpm8JoQcSzKrSw9xIrATqzvNAGYgsuw8+v5/kEXbKAjowl3XPkuIt4lSu4NMi5U2LoYWf0itXDzUw1ilxdltNqzl5bi4ubV0Vs5J/NirsZw4weornUM2PzfVGxwgCi20saWxZG8c5Y6BvOO7koAiMxVY+abdwtOOkx/wBFaXaAA0dhv//N8ccLVSFD4Yrwpvgn0Hs3ZUCT/me/J4uI4H26u2+Eo19TBWuaBpdTq0F2mQB2i37HfiS8t5df1ejDY909cWoXXAFyPc6X8yhHKHL/naIpb5L+apEzeit9mh3enH6Vi+hBk9RqHTaPg+PZePb3iQ7hnHGXe0iCJvLQnsoXPeErowhc5Jz4Pr1yAlhNX62VaUKirQK0oD2R2S3Ul5eLjo6RTkbAtvEqC3G7htZQFuFRIpK3jmmxwm+jqnECzUFuMQdt7vsAgXS+0dpTIL9/PosZTqFd7+rPf2Z2+nAj7gtqrVXTnoDPAfj8Aq9OifyW66i1VaBRXoFaWBnvzpAN/uOAnAZzP7M7xzACcP7GfGxq24VTirUyoKPgdZQuAbi9CZDQSXWNH++AJZ3hVVx/lnv3/SxbcLFrsFgcDXFICHKQKbBAG4aDTcfziR/bm1BHIhmO0yG1FczuvNcM3KxU31wVaUBjLqnB+bW7QrGP5tB2RxFsHBwdj1hZSZUnAIKzrXIehcLmPZsg8QGoHW3cDtPe6oOsbQ0KHM6DqD/kH9GRI6hMGhg+nk3Z5go4FwFwNhLgb8DDr+1y2ScSyucf4B/ZcQEDCOq/t/xaQxy7BYcpr1+pWLT71L9EIILbATSJFSThBCvApMBCxAPHCrlDL/L2nCgS+BIMABzJNSvtVIeVeUFvHMxK4Y9VoCN2cA4LDbCA8P5+a/T2P+/PmUuJ3AragtQRFJePhXV8U80PsBRrcZTZApCC8Xr3qdy6zTcmPHSRQe+61qXUHBLiyWHDRCYrMVUlqagMFwYXQsUy5M9W51I4SYDfQDPCoD/Ricc8DahBAvA0gp//WXNMFAsJRytxDCHdgFXCulPHymc6lWN8pFw24Dbc1hEY4fP46HhwceHh4YjcZGOU15eSpp6T+RmfEbxSVHAWjb9kFCgqfg4hKCdEisFXZKCiowexkviI5mSvM671Y3QogwYDzwAjAbQEq54pRdtgKn9e6QUqYBaZWvi4QQMUAocMZArygXDW3Nj5AQgnbtamlSc55cXEJoG3kf4WEzSEz8EL3Bl4jwW4ndmcHWnzdTmF1eY/+73r4CnUHb6PlQLk71/dqfCzwK1DXc3m3A6Q2DTyGEiAR6A9vq2D4LmAUQERFRz2wpyqVFp3OjXbt/Vi0XZpedFuQBFeSVGs76MFYIMQHIlFLuqmP7k4ANWHCGY7gBi4CHpJSFte0jpZwnpewnpezn768mTVaUs7FbHRzd6pz0ZOxd1Z2nDC4qyCs11afVzRBgkhDiBPAtMEIIMR9ACDEDmADcLOuo7BdC6HEG+QVSyh8bJdeKolBSUEFeeikAyz46iH+E8we3pdyOdFx4Pd6VlnPWQC+lfFxKGSaljASm4XwAO10IMRb4FzBJSllaW1rhHIzjf0CMlPKNRsy3olzyPPxc6TW6upozK6kIKSW28j18ePctOOz2FsydciE5n3b07+Kss18phNgrhPgQQAgRIoRYWrnPEOAWnL8C9lb+javjeIqiNNDg69rRvm/1BCbSlsxAdy/6u47hfw/OasGcKRcSNaiZolzkpJTYrQ50Bi3lBcVkv7iHk5psNlRs5qpxo+k5ckxLZ1FpBmdqXql6xirKRU4IUdXKxsXTjaBH+rHeJYZSdzPLFnxOenzsWY6gtHYq0CtKK6PzdeW+2f+HPjcDbWkxC554mIyE06dpVC4dKtArSivk5ubGY2+8g9nDA4Df31VDn13KVD9pRWmltDodM9/4gF1LfqJd34EtnR2lBalAryitmKubO0On/b2ls6G0MFV1oygXqYKC3WRlrUBK1V5eOTNVoleUi1Bp6Qn27J2J3V6Cq2sEEeF3EBw8Ga3WpaWzplyAVKBXlItQYuJH2O0l+PpcjtVWwNFjT3M8YS7hYTMIC5uOXu/V0lm8YJSVlXHy5ElOnjxJcXExVquVo0ePYrVa6devHx4eHvj4+BAQEICvry9abesbK0h1mFKUi1BJyXF27/kbdnsJV1y+n/z87SQmzSMnZy1arYmQ4KlERNyOi0tIjXRSSsr27KUiNhavqTfgHKWkdcrOzubXX38lMTERAI1Gg8lkwmAwkJubW2saHx8f7rzzTlxdXZszq43ivMejVxTlwmI2R6HTuWOxZAHg7T0Qb++BFBcfJTFpHskpX5GSuhBvr/707PkJu3Oy+fqXPUTtMAAwfO0zuPbqhUunji15GU0mNzeXN955nwS7L8E6E3feOJH27duj1+tP29disZCWlsb+/fvZtWsXv//+O9dff30L5LrpqECv1Iu1ws7G72Mpyi1n/L090OrUc/yWZjZ3oLT0OIdjHsFo8Edv8EE67Ag0aDQu2O0l5OSuZ/WajtzJF5RGBfFK0ROU7O6BAEp37Wy1gT4+Pp5V/j3ITNbjE1ZMh3XPEx1dcyR1q9XKtm3bOHjwIOnp6VXrk5OTmzu7TU4FeqVeNn53jMOb0gAoL7Zi9mqcKfKUc9eh/RMINOTlbcFiyUVKCwBGQyCeHr3Iy9mK1Dhb5DzCf0mW4QRG5dA1fTyF/ELGf57De9o0hKb1fWn36tWLq5Mz+djPh8Heayk95ElOTg6+vs65dePj4/nqq68ACA0NZfjw4QQEBGA0GvGo7GTWmqhAr5zRL689T/TQKzm8yVK1Lje9RAX6C4Craxjdu78LVA5sZi8GQKdzjkuf8dm7JO59G68uwzHP34Vlcgim1ClUFCbyZ838sf4D6Lhje6sL9nq9nmevG8+zQFycP4v2LuKLL77grrvuwmazVQV5gNtuu61VPoA9Vev631UaVUVpCXE7trJu/qc11gdE1DWjpNJShBDodO5VQR6A9CLcl2sJH3Qv/p5BjPlwE36LP8W9czZRS34FwFFSwpEuXXGU1jqlRKvQvn17brnlFgoLC3n11Vd58803AQgMDOSZZ55h8eLFLFx4xplQL3qq1Y1yRvnpaRjNZpKPllGcV0FoRy8C2rS+n7atjTUlhbiRowDwmTmT3M8/B6BzzOGqljaOkhKO9q1upNH2l59x6dSp2fPaXJYtW8bWrVvr3D5nzpzmy0wTaJRhioUQWiHEHiHEksrlV4UQR4QQ+4UQPwkhvOpIN1YIcVQIESeEeOycrkBpMV5Bwbi6e9ChXyC9R0eoIH+x0OkwDRiA1tubvAXOh5Ae48fXaE4pHXoif15etZxwzbUc/GZ+s2e1uYwdO5aZM2fWuu2hhx5q1rw0t3qX6IUQs4F+gIeUcoIQYgzOaQVtQoiXAaSU//pLGi1wDBgNJAM7gJuklIfPdC5VoleUhvvzs1yftvH2EitpzzlLt3KaBwfvm0VkTiEnfD0w3XMXV0y/rUnz2pLsdjvHjx+nsLCQgIAAwsLCWkV/gvMu0QshwoDxwCd/rpNSrpBS2ioXtwJhtSQdAMRJKY9LZ5OAb4FrGpJ5RVHqJ/bmaRyJ7kJM52jee+82YnJi6ty3dGdG1evvX/w3h8P8WR0dwZEQX/YuW8KFWKXbWLRaLR06dKBv376Eh4e3iiB/NvVtdTMXeBTnHLG1uQ2o7WlGKHDylOVkoNbxUoUQs4BZABEREbXtoihKHewOO/bd+6uW3ZZvZarbVA7MOFDr/ubLgslaGsPe7NVIJKNnPUDXK0ZybOtGPPwDL4ngdyk5a4leCDEByJRS7qpj+5OADVhQ2+Za1tVaVJBSzpNS9pNS9vP39z9bthRFOYVEcss/tCwaLNgfKZh/pYbojMF8MW85yUdO7+6vMWgR07yxRkimvziXHiOvQqvTET30SkI7RbfAFShNqT4l+iHAJCHEOMAF8BBCzJdSThdCzAAmACNl7b/1koHwU5bDgNTzzbSiKDXpNDpW3rKBm3xu4rfyAnrGXkX39MspBn7ZvZdOg4IY+fdohKa67NW2dz/a9q61SldpZRrUvFIIcSXwz8qHsWOBN4ArpJRZdeyvw/kwdiSQgvNh7N+klIfOdB71MFZRzs2B+KOsfzWlarn78FAOrHEu9726DYOuaddSWWv1pJQ4CgqwZWXV+LMXFqFxc8P7b39D62ZusvM31aBm7wJGYGVlfd5WKeXdQogQ4BMp5bjKFjn3A8sBLfDp2YK8oijnrmBPzdrYA2tSMJp0VJTaMHuq3syNpfzYMfK/XYgtKxNbZmVQz85GWiyn76zTgc1GRWwsoa++0vyZRXWYUpRWxWqxk5VUhE6vwWjSYymz4Rfuph6uNrK0p54i/8efMEa1RefvX/Wn9fOrsazzD0DrZibzzbnkfPQRpgEDaPPlF02SJzVMsaI0kMVioaysDE9Pz5bOSoPoDVpC2nu1dDZaPVtOLrrAANouXlyvL1H/B+4n56OPKN2+HUdZGZpmHu9ejXWjKLX473//y5tvvklhcu0TVCiXNvNll2FLTSP/u++xF5cg7XasGRlUxMeT8+lnnLhxGvaioqr9hU5HRGVJPveLL5s9v6pEryhnkPbuLjxeGt3S2VAuMKb+zhqS9GeeIf2ZZxBGI7KiosY+WW++SdDTT1ctmwcMwG3kSHI++QSvG6ei8/ZutvyqEr2i1OLRa+7lxvLBmFGTbSun01WOa/8nWVFB4FP/rrHOlpXt/Dcnh4Jff8WakkLAww/hKC0l58OPmi2voB7GKkqdKhILMYS712h7rigADouFoz16Vi173XADwc/9B0dpKUf79K0zXZuvviT/p58pXLKE9mtWn/aFcT4aZfRKRbnUGNt4qCCv1EpjMFS99pg0keDn/uNcbzIRtXIFRT1Gsb3f46y+8j3ig9uR7eZ8+OrSrRu+d9yBtFjI++bb5stvs51JURSlFXK/8soay4XSix0+11HsFoaUVo4GwK62wXTYvAmNqyvGqLa4XXklefPn4ygra5Y8qkCvKAolFbaz76TUymPcuBrLfmFuBLSpHP9R6NC6XkFYcQCJN0+v2sf39tuw5+dT8OuvzZJH1epGueRIKbFlllKRWIi5TyBCd2mXd1YcSmfWV7t4aFQHwrxNuOg1TOgR0tLZuuBp3N1xFBVRtGYN7sOHV60XGsENj/dHSslnL7xAks1GeWAAheWerH70Uax2O+VCQ+G0G+HwYeY0Q15VoFcuOYUrEila4xw9W5bZcL8i/CwpWp8Km51//eAc1nhTfA4Ac1fFVm130WkZ1SWwRfJ2sWi3fBlpjz+BsW3bWrfbbDaSbM5fShqHgwIXIzohMOj1+Ol0FFbul56eTlBQUJPmVQV65ZIirQ6K1iVXLeuCmm6QqQvZ4dRCft6biq/ZQE5J9fgso6ID2HuygDu+3MmDIzvw8OiOLZjLC5vOx4fwjz6se7tOh8FgoFevXoz7S/UOQGFhIW+88QZHjhxp8kB/af9mVS45OV/HgEPiNjQUraeB3G+OYs1ungdiLcHhkCRklyClpNxq53hWMSsOpbNgWxIA3919GeO6B9E91JNVs6/gkxn92fgvZzXEW3/Ekl1ccabDK2cghMDHx4e8vLxat3t4eNCmTRsOHWr6cR5ViV65ZEiHpDzGOaSB51WRmAcEkfHWbjLm7sZ9aCieYyNbNoNN4MmfD/LN9qQ6t0f4mHj/5prtvnUagUGnwWZ34Gs21JFSqQ8fHx/S09Nr3Waz2cjPL6CgIJ+0tDSCg4ObLB8q0CuXDKERoBVgl5TH5+Pa2YeAu3tS8HsCRWtP4n55KBqTvqWz2WjeWHG0KsiP6BxAv0hvHA5JoIcL4T4mfM0G9NrTf9TrtBpuHRLJR+uO89YfsTw0SlXfnCs/Pz9iYmKwWq3o9c731oYNG/jjjz/ou2s9GYZovu0wjm6bNjFlypQmy4cK9Molw5JaDHZnT3BLUiGunX0whLtj6hNIxfECyo7kYu5z8T+A/GZ7Eo//6JwrdlgHPz7+ez9c9Np6pZVSMmfxIb7Ykgg4H9CqQH/uPD09kVJSUlKCl5cXAH/88Qc2t6W0jy2iPWl822EcW4/EMlnKJhtOut519EIIrRBijxBiSeXyDUKIQ0IIhxCizvnIhBAPV+53UAjxjRBCDR6itIjS3ZlkiUKOadIw9fDHmlVK0YYU7PnlAOR9dwxLSnEL5/L8bIrL5vEfDxDq5cqYLoF8dEvfegf55LxSJr27qSrIdwp0Z+XDlzdldls9jcYZYufOncuePXsAGD58OAPlSd4fr+HrKwXW/j5o/UooLCw806HOS0NK9A8CMYBH5fJB4HqgztF5hBChwP8BXaSUZUKI74BpwOfnlFtFOQ+uXXxZsmMXduGg0wdhyAr7afuUH8vDEOrWArk7fw6H5B/f7SPcx5XF9w3F+yz161a7AwFoNQIhBENfXgPAQ6M68MCIDmgEasKS83TgwIGq16tXr6Z3794MHjyYF9bcia+7ho7xu/j4zYd5vc9MVi/cxXWzRjRJPuoV6IUQYcB44AVgNoCUMqZyW33O4SqEsAIm1OTgSgsp2ZnOGGtPjL4mZFZ1kA9+YqDzt60DNO4Xbx394bRC0gudv06eWXyIUoudcqudUovtlNd2yix2yqx2bA5nNZYQ1KirV1U1jcNqtZIVl0aI9CZdk09RURG//fYbx9dY8C++kqyg9WTq7KSF+qErs7MxeRvX0YKBHpgLPAq4N+TgUsoUIcRrQBJQBqyQUq6obV8hxCxgFkBERERDTqMo9VJ+LI9Qhw8+IzqRu/AoAJ4TotB6tI6WJd5mA0KAlLA/OR9Xgw5XvQaTQYevmxFXvRaTQYurQYur3vkHsGBbEhlF5YR6uVJhs2N3SLT1GMxN2u0U/PwL5qFD0Ade/M82GlN+fj5z584FDRRR3Xx3x44d+JT1B6B3797sKi2jKNNCnp8vZpHdZPk5a6AXQkwAMqWUu4QQVzbk4EIIb+AaoC2QD3wvhJgupZz/132llPOAeeAcprgh51EuQVJCRSGU5kLCevBtD5FDzphE5+eKpdhK7sKjCKOWwIf6oPNuPY+MQr1cSXhxfIPSPP3LQdILy5l1eRTBni48++thlh1MZ3yPszf1Kz9yhLQnnwTAZ8YM/B9+CI1L67mf52Pu3LlVr/v27IPe1cC+ffsoKysj128H4yeMRwgP9phc8Yx0ZRxHAGeTS52u8dvI1OeIQ4BJQohxgAvgIYSYL6WcfpZ0AKOABCllFoAQ4kdgMHBaoFcuPSeLTrI+eT1hbmHoNDoKKgpIzT7MTcYQzHY7lOZAWa4zmJflOf/9c11ZHjj+MhDXnWsgtE+d53MbEkruiUI0Zh3+d/dsVUH+XNm2f8ob+qM8svE+7JVVOflllrOkcnKcMlVe7hdfkPvFF5TO/jt9Zz3eJHm9WI0YMxKz2UxERAS7d+8mLi6O3377DYC77rqLvXv3sm3bNgDKyspwd29QxUm9NGjikcoS/T+llBNOWbe2ct1pM4UIIQYCnwL9cVbdfA7slFK+c6bzqIlHLg2zVsxiS9qWmiulpKPFyqLUUzqZGNzAKwJcfcBU+ed6yr+/3OvcL6g73L2xzvNJKbHnVaD1NqqHjJWOvDqaziXbebbPJjoEeuBq0DAqOhB3l7M/q9j12+eY/vFyjXXL+gWy7YZgnhn6NB29O6LV1K/FT2uUnJzMp59+Srdu3bj++usB53vw2WefrTPNXXfddc4dp8408cg5/0YQQlwHvAP4A78JIfZKKa8SQoQAn0gpx0kptwkhfgB2AzZgD5XVM4ry32H/5c1db7I4fnHVuv8smoJRHkb2TacqFjvscM17ztL6to8gdgWk7oGhD0PnidWBflTdHyBwNhzQ+VycpfiioiLWrFlDr169zukZlqPCBgiETkBlKxtrRgluOU+T082LZyZ1a9DxpJQ8V/EjwdM9GaqP5hfLDganvUxnRxz2XaUs2rYU79w0ug4Lof/4tpi9jA3O88UuLCzMWQ+/axcjR47EYrGwZs2aGvu4uBQSFBxL4oleSKklLy+vSXrIqqkElQtCXF4cv8b+iGleJ7wqTnJjuydh1BzQm2HD62Atg45XwYHvnAm0BrBbIKw/JO+ADlfBzd+16DU0FSklb775JoWFhQwePJgxY8Y0KH15XB7Znxw84z6hzw9p0HDNBRUFDP12KACLJi2io3dH3n7kNwZbctnkmU6epgT/9Oo2+D4hZkI7eBHayZuIbr7oDZdGST8rK4uPP/4YX19fOnXqxNq1a6u2WTVavhg6lhtZwPDy1ezcfj2jRo1i6NCh53SuJinRK0pjau/dnocHPIqtpxVd8kbIfR363e5s+9duOKx9CY6vde486D7QaGDzO84gD9yR1psx6bkM9nIjzKV1tKL504kTJ6o605hMJgBycnIwGAz1qs/VB9Q+QqdLJ2/KjzoH3Mr65AABd/esdb/aeBo9eWv4W7yw9QXuWH4Hzw99ntufG84nLyxCm94Zf031OQPauGNw1XFgXQoH1qXUOM7l0zriFWDCM8AVNx8XNK1s6kZ/f3+uu+46Fi5cSNu2bZkyZQo//PADACmDhmITehYwkxK9G0FQ5wBo50uV6JWLh5SQnwRugaB3gcI0inITuXfeGtbrelEx3PmTN/GKHhg1rWdg1rlz51JUVITd7mz7Hx0dTUxMDAADBw5k1KhRVeOo1EU6JCXb0shYtZ70rp9SlNITXXpfOttDAfC6tj1ugxpeZZBYmMh9f9xHYmEiOqEjwBRA+OG+9E4dhcOjgr89NAyfYDNCCGI2p7H6y5gzHu+HwW7EhBu40tsdL70WV60GF40Go0bgqtHgqtVg0mowaZz/5tns9HY30cvD1OC8N5dffvmFPXv2EBwcTFpaGgCL2i4CYLz/3fzgMoDJRQdw3ZOEXq/nycqWTA2lSvRK6yAEhxzF/Pjew4SvOcJ74zWUurqRM+qDGrv13nyI97u04UofjzoOdPFwjnCYT5s2bdDpdMTHx1cFeYBt27axbds2Jk6cSO/evau63P+V0AjcLgvBGtKRE/HxZBQbSMgxE2UPpO1/rzznSdDbeLRh0aRFrElaw9G8o6SXpJOuySK/IB2vwiC+/c92jCadc3q9SA8m/l9Pfn17X41jhA8P4eQaZz/KDC9nlU5ZwkZmHv+G+02zKe4cUL2zdNC38BAZRn+SXarHcL8n3J+ZoX60cb2wngXY7XaOHHE2nfwzyAMEWHz4Iv55iPuJl64182uJP7tIwmq1Nkk+VIleuajcs+oexr2wlvZpMPsOLQe7PIrVtQc+OtgyqBt3HUpkbZ6z2d+2QdEX3Ae/oeLj4/nqq6+YNGkSHTp0YP369fj6+rJs2TJ69uxJTk4OycnOiVS6d+/O5MmTz3pMu72ctWs3UVFSzlUjx+Bw1VGUv5nk5K9o3/5fmEy1z5jUUMV5FSQezCYzqYic5GKykopw2GuPN2leWhYOc6fI5PyiOrj5Gvys+Qwve5UoTQJx/gMpclQwNWoZj+3/EYCgK9bVOEa4i4Ht/TogzvLrprkVFhaydu1aigsL8SvJI27XNgqDQplumYCrZj1jh/XhTXd3Vq1aBcCcOXPO6TyqRK+0Gm9e+SaDE/sRkgPJ/gKraw8Acm0wNzGDmBJnL8R/tQ26aOvqpdWBvdiCvchC7q5k3B0uREdH4+rqytixY3nuuedIcEtgY/pGfrvtNwwGA/PmzePAgQNIKRk+fDi+vr51Hl+rdWHkyJFVy6Fr9gJmXpCx5BdMJTBwIhUVGbi7dyE87DYS8+y08TGhq2VI4zNx8zbSdVgoXSuXSwstHNqQwsF1KZQWWpBRbmw22AjS6fjH33vQp6yMew87B1S7us+HhOSmEb4jni5pB9gUnAxSQ0KBH/8LuQ57SW9uWv4LhWY3unu4obHbGZQYy5FblwEQfeTMVUTNyajT4Z6XQezSX/izTH/Sw59+49xAODu4hYV5NWkeVKBXLiouOhesOkFiZY97kyykVDiraD44mcUgTzPzukbS06WQPbun0qnTc7i7dW7BHNfNUWrFlleBo9xG4cpE7PnO17L8lHF40HClsTuurq5A9dhSKeYUMkwZvPXOP+jccQzFxc5RNw8ePEhKSgr/93//1+C+AgYsWK25JCd/QZnNhYVrvcm2rycm3dkxzdukZ/4dA2nn71bvETFPZfIw0H98W3qNieCpLfF8bithUmAAL3SOwFWr4XoPF64P9GZ3YQnjdsHJkGBG+xdAGug99wJw+fIhDLJHYY2ZzzBLE4w0KqWzY56l2NlPw9jwzktSSopzc7BZLcRu28zOJT9RVlhQY5+ok7HM+P5djkV1pX9JDu6d72usK6iVCvTKRefAjAMcLzhOTlkO8Y62JJZbGe3rgbdeR1uTs6om5siLHC1I47pPUtGmx7Pmn1fS1u/Cmh82/Y1dOIpr1sm6DQ5B46ZH625AmHVkfXkI4VldFaHRaLjiiitgHQy7/CtujrgDgLuLdlft4+7uftYgv+xgOi/9HsOJnFJcgK5+MXj3yAEdDBy4jJeWbGNDijdh3lom9wli0e5k8kqtjH97I0adhi2Pj8TnHGefOlJewef2Um4I9uGtzhFo/pLXPh5mOppcOFZaTnJwOE98Fc+4fSCBtj88ANJB9keZFK/6g9C5c3Ht3g10OjRmM8f69W94hvJOwMEfIW0vJG6BkszqbbPWQUivBh1u64/fsvm7BVXL4V17cPnfZuJwOPjmqX9Wrb9+zFWs+m4hy/1H0nnj1obnuwFUoFcuSlGeUUR5RlHXx7pt2wc4YvVHGpzVDZ6uF1a9LYA+yIwlsRDfv3dBY9KjDzIjtNVBL+d4Olo06P8ygfnw4cNZt24daWntIQRcLeU1tnt7e9d5ztT8Ml5YGsNv+9MQOPi7diWr7H05lB3N/qyuXN+3EwdOpvL9fhODw5P5+r67AHjthh4k5pTy7K+HWHM0i+NZxfiYfc7pujMszl8IwQY9J8osRJlOf46yfmBn4o/Fc+I/zsm38++5j/43T0Pn5wdA+Lvv1nps7+nTyf+ugf0plj8JR5Y4X3e9DsIGwNGlcGID/HI/3FN3b+vaxG7dhH+btvS+eiK+oeEEd+hc9cX7j4XO89gsFnQGA7dv1VIsXJi9C2a6VDefbWwq0CutkosxiOu6P8R13Vs6J7Uryi0nXge7TnxH1E9duPrRu07bR0qwYce838YR2xaCr+pEdmoW6376gb+JVfwaO4i7Y39m0KBBnFoeTEhIqPWc8VnFjHzd+QBzunYlz+s/A2CA5gj3Wx9k3oGZpJbHsCS2GE9jOf+dUt0xSwhBpJ8Zm0PiYzbQIfDcx2OxOhwAvJ2UydtJmfR0d2WEjwdd3FwxazVohUCXno7P9ZMIqUzj9cF7xH7wHq69euF9899wv+oqNIaavyik1UrRihWYBg5sWIZGPl0d6NsMgYBoWDXHudztugYdKi89laykE3QcNJS4HVvpOHAIJ/bt5ujm9fQcM46vn/xH1b7Rw4bTPy+bNT5DmRFZAunQvn37huW9nlSgV5RmIqXk8MZUjm5NJy2+AOkoocIST2p8SK37+7ULQt4hyfrqIB6HbRQdPoQRGMPlCPoxwfgC34gR5Obm8tRTT/Hrkt/4fEc60XU0pHPRa4kO9iAmrZBi6Vq1vkxWl6iXxEZj1pfw+nUBRAbWbH2zZH8qG2Kz+dfYzuf1C+lqfy82DexMUpmF2NJyfsjI463EDByn7DN96Y/cXvnafMXl6Pz8MIRHkD1vHqmPPIrmuefxGDsWz4kTcO3bF6HRsOnT99jX5Rm6RdupzyARBRUFeOSnIL6ZVr1SOuCLic4xlGashcAu9b4um9XKpw/OAiBhvxkpdbx7641V2w+t+6PG/jEb1tAN6FZwgKiuN7APiIqKqvf5GkIFekVpJmVFVtYucI6DP/CaKDr0C2DvglAOHsknPz4fr3ZegLNzk9AIpF3i3z4Yn6cCOPnOdqx6iYg04bKrBFlmwjX8TUj+laioKLRaLR6dBrJ3y06ksfb+A6Fersy7pS/DXlnDz46hHK0IZ4HhBZbJ3ui9NhNu7MIvd43HzdWzRh2/lJL318bz2oqj9Inw4tYhked9L9qZXGhncmG4rwezwgMoszuIKy2nwiEptTtY73sLmdeMY9iAPjVK7uahQ8maOxdhMnLCdSHlqfPRHtbhoevEwUPOr4aDMVraHMjGYZcERXli8jCw7uQ6Pj34Ka56VwJNgSw/sZwSawlDSsv4MD+rOmO/P+r89+6N4NGwDmQFGZUD8QkjQheCtB6vVzqDxoUhQb0ZOHJ0k4xzA6odvaI0mXKr/bTWKeu+PsrB9SnMeHEIbt5G8hIK+PrlXfTo5MXQh3qT81UM5YdzaqTxHNcWnb8rGlcdQq/FUWYj+5MDuHb3w/fm6Br7rjqcQZ823nU+KLXZHTz24wF+2OVse29GMtMYR4nI52pLb0IdPiQEGLGNi6RvpDceLnpeW36Ud9fEMalnCK9M6XFOLW5qYy8ooCIujorYOKSlAuHigi07m4qYGHzvvhvXrl1rTSel5LP9H9Am5/WqdQ6bgdhf3kDaa9b3e/q7Evw3C//c+RAeBg8KKgqwy+pWTYNLy/goIwv6zoRdnztXdhoHN31zTte0f9UyzN7e+IZFYPbyQW80UlFayo7Fi9j208KaOwuJX5dcojJuoa17d9xn++IZUP9fEH+l2tErSjMb+vJqkvNKealrCpOvnUJhYSlegcG4uDmrPOL2pNFrRCTebT0J9DIQF1fAZUWW04I8QMHS2uvcRS0Dg43qcuaZnnRaDc/3bsN9pVr0Mc5xVRKtEaw05GPTacACIZnljPx8BxoBbXzNJGSX4G3S89a0Xo0yvLMtO5vEm6djSUysc5+SzVvotKv2wp7FYeHNvR/gpnHlH4HleOskH6W/yvC/BPlB10ax9efjZL9jQd/blWk9pjEyYiQ/Pnwt12yVLB8seSiisjT/Z5AP6gE3nvt0GT1GjT1tndFkYui0W2jffxALnngYf5dwenpfSfakJ3DYIf7H9dg6Ludyn4WnH7CRqECvKI3scGohyXlleHd6ghcckmmvP8r8IyOxSQsT//0eABu/i2fj4kNcc+8AOvYLZMOqkyTvy8brijCK1yVXHUtj0uE9pSNadwMlO9Mp2VY9Tr/3dQ1/cCftDrI/OcCpNextHP488/QzFK05SeHKRAIf6svXxWVsS8hlY1w2Cdkl3H1Fu0Ybw186HKcF+Q5bNiPLyylet570OXNwlJSQ/+NPeF3vfBiaXZbNJwc+Ia04jcSCEwAUOwTLLV2wFMRid8mgwBSFZ6mzhkJKyf41SQCUGgqo0JXw/t73+WDvB/y9skB/Y0mRc4A8n7bOGcq0eggfCE00hn5Quw7837vfkfH6Luc14TxV+A07GHTZerS6phtCWwV6pdWrKC1l/6rfsVktRPbsQ1C7jrUHLSlh95ew92s4uRXajYRbfmzw+XYm5gJg0ziDTpm9Dz7GYDLLEynIiiXXbxduBR0wlHux+I0DVelit6Uz/LauNQK9o9SGLbcc1y6+GMLdMQ8MJv+XeLxv6NigYYWrDwgaN32N9vshcy7DllVK4ZqTuHT2wS3IzGDMDG7vx8OjG3+i8OI1awFIDRpEYsRVdD66gJyvFhD4f/fjNWUy5UdiyP92Ielz5uA5aSJCp2Nn+k4WxCzgrqV27t4n+e9UDXvbadiak4AzjM0loZueT7stZNO8rViKFlKRD0bPe0j1iMWhcT7qvbvn3fQe1Ytw9064edTde7ip/BnkAdoE3kdixnv07PExrq6hTXpeFeiVVi0n5SSfz76nannzdwsIaNuOW156y7lCSlj+BBQkQ8zimonj/4DiLHDzb9A5J/cJ4+lfDlEU8xIf6N/AVbuTZNcQDOWw/qO5dJ4yg0MH92OxdiM0t7ot+rG4AqJf2fGXo0m07tUfU0OIGwH31H844b8Seg0h/x5E5of7sJxwDn1syyojd+FRNC5avCd3OOdj15e272Xs6PMoRR5tANjT+2H2HIa78/MpWLyY/G+dVRj+Dz2EqJw/dUTECIaGDkVndzYP1Z8yi+QDvR+goKIA0//6smX7SRA1+x2EDHFhXvd59Avqh17Tcv0pdsWvZk3y/fi3Gce4qBmEdZ1N+66zm+Xc9Q70QggtsBNIkVJOEELcAMwBooEBtU0lWJnOC/gE6Iazc9ttUsotte2rKI0t7diRqtd3ffglH939dzIT4rFZrej0erCVw9b3ayb6Zxzs+xpWPg1FqQ0O9Gajjpcnd+dfiw5wxNGGq7U7ERgJbt+J8Q8+grtfAIWFhezLSWR5iI2+yTomX9UPv3KJSa9h49adJMYsYrguGg6uJOD+rxvjVtTgfX0HMt5wli4z39sLgN/t3dC616+3a2npCWy2Qjw8elStS08o4OThXCpKbQye3L7OseXXL8+lwGREOEoRGmcHocgTS4kf+yRuV1RPVuJ57TVVrw1aAx+M+oC4vnHszdrLw96diMuP49uj33Jt+2sJMAXw3v9WA6DReuHq+w9mvjQEk4cBaNiE6U2lfMG7jN8pScz6He/ZzzXruRtSon8QiAH+bLt1ELge+Ogs6d4ClkkppwghDMCFO3C00urkpJxEo9Xyf18uQqvT4RUYTH5GGm9Nv47hM++iz4DKVg4T3oQlDztfl2Y7x7wH53y1Z2F3SArLrHif0tLlxv4ROCTEZbbFevUnPPaXAcEGDhzIyR9+4Ip2euKzt6Pf9BqfBU1gQPvFDLr3La72XoAtK4v873zQh9Tezv58FG9OrbFs6huIS4e6e9T+yW6vIOHE2yQmOnusDhu6HYPBl5zUYha9XF0tkRZfwLCpHQiK8qyRfuGcx0iOqTnbldAGEnViM3agYPGvmPr1I+g/z6LzOb3nbXvv9rT3dj6b6O7fnes6VHdo6n5FaNXEJlP+1bcyyF8YSnakE1l6DwydS3ff29H5up49USOqV6AXQoTh/Fp8AZgNIKWMqdx2pnQewOXAzMo0FqB+U8wrSiNw2O2AIPtkIoFt2/H3V95hw7dfsOf3X1nz+UfkLy9mhBfVQR7AMxyO/u587eJZy1EhJb+M2Qv3cjSjiPxSKyCJfeFq9NrqB3k3Dai7205IZfA+cOAAnUilky2Gjol+LJZ92VQ4E7+QW7i337/xv79pBrvymhiFzscFe4kVt8Eh6DzrN5zzyZOfVQV5gN17bsbTozfZx/oB3nj4uVCYXU7miUK+e30j/W70Y/Dl1T1VpXScdkxfewnGjh3xGD8er6k3oDvDEA5nctn17Yns4Yd3sBn3C2xu4ILfE5C4gN9jeJ3DQ/TzVd8S/VzgUaCh/Z6jgCzgMyFET2AX8KCUsuSvOwohZgGzgHOa/FhRajPwuqkc3byeDV9/zpQnn0Pv4sIV02/HaDJzaN0f+BFXM8Gwf0JhCmx5F6t7OG+uTefGQa608XXW+0opOZJexA0fbqG4wsZVXQNZfigDrek4vb/qxcrrthLsefbB0zw9q79AjhHF+2GjOeIIYkLbZTyR6oLI/5ab2o3G27uB3fnrSWg1uF8e1uB0QcHXknTyf9hsBYSH30ZR0SEys1Zw8PfRAPS8uoLOfS/n44fWU+h1mBWrS+hAINZgT0I6eDPt2Vca+1Kq6I1aIro2/wPW+jD3D6Ko8iG7S2XHuOZ01sf2QogJQKaUctfZ9q2FDugDfCCl7A2UAI/VtqOUcp6Usp+Usp+/f8PqRBWlLiYPTyJ79iXnZHVzPq1Ox5Cp05n13mdEeFbUTFCeDye3Q2kOH+T15f0Nidz11S5KLTZ2JebR57mVXP3WBjQCPp3Zj49ucfZPsZe2AwTT5tVvFEKtVssjjzwCgEQQ4jed2wd6odVIHgsq59mQMozGoLMc5fxkFpXT0A6TBr033t6DkNJObu4mvLwG0KPH/0A42yx26N0bg4sOBHQt70hvWyTlS5P4+fU9WC32sxy99XJUXrvGw4DWt/l/bdSnRD8EmCSEGAe4AB5CiPlSyun1SJsMJEspt1Uu/0AdgV5Rmop3cAiH1q3CUlaKwbX6EZGjJA9PcmvufPmj4OpN3Kr/cVfJEioiRvB+Aox5cz3Jec5JTZ6d1JURnQMI9zFRUFbdTLH4yIsIFxsVNjtG3dnbYpvNZq699loSEhIYP348Ot01ZGdfTmbWcoKDJmMytWmcG/AXFTY7766O453Vzl8zbf3MPDSqA9f0OnsTv4OHHiYraznH6MSzJc8xtXA+0Yv/wFZ+JTpjDz6dvZv7PhzBtQ/15td39jEAL4Re4Bvgir6WDl6XAmlzUH7I2RHO2Naz0fojNMRZS/RSysellGFSykhgGrC6nkEeKWU6cFII0aly1Ujg8LlmVlHOhU+Is4oiL63mA8jS3AxO+8zlxIHOQPCs78nW+HFD+mssnHVZjW7/MwZHEu7j/MJwOKpLxBE+JorKbUz5wFmtUx+9evXiuuuuQ6/XI4TA338MXbu8jo/P4HO40rMrqbAx6Z1NVUH+z3UPfruXLfGn98r9q6ys5RygB8+K/wJg3+CgNMOGrXRVjf18Qs206ebLlhI78R19mPrsoMa9kItI0bpk7IXOR5M675aZ2vIcelw4CSGuE0IkA5cBvwkhlleuDxFCLD1l1weABUKI/UAv4L/nkV9FaTDvEGdJNW5HzVa95tCO5Fmr69PtUrD1tYexLpuD2cufEx1mEOlIIsiWzMqHq5v97UrMqz622cCJl8Zz4qXxrH90OHNv7MWBlAK+3Z7UxFd1bv44ksnRjCKu6x1ale9nJznHlCm3nr1qJdUwiJfEMwAIh6RDYgYAGn07ALyDTGxbfJwFT2/lxIFshkxpzxW3dW2RUmxLs+VXkPdTLIUrE3Hp4nx2IIwt86umQR2mpJRrgbWVr38Cfqpln1Rg3CnLe4FaB9pRlObgFegcEXDLT98Te/QI3fr0o/fYiWh1Ooo8uuBd5uykdKDkSZb5lbByk42nRlUgdM7meTrsCCGYqjdijXTnjvc388QNPZjcJ4xHF+3H183A41c7BxfrHOxsr3A0vagFrvTs/qxqmj6ousGDvrLp56yvdnLw2avqrHZKLbfwiNX5XMGvwM49ywqwa4agMWvQGpw9aPPSS9n5+wnadPVl8PXt8Qm5sGb1ai6OMhvpL20HwKWzD9rKpp76wJa5H6pnrNLq6SqHua0ICOWw3ZW3UqyEfPI1P996I/ro0bB7B3k97mP5Vl9cHFZyLXpI24eb3dlzFFcf7A7JiXAT66OM3JLkziPf7+eRH/ZXneOeK9ohJSyqHBXy+13JzBgcSbfQ2ptnNjcpJa+vOMa7a5xVNnuS8vEyGfgjJoMDKc7rDPVyxXCGCcBDXAz8OyqY54+nIQWUGwUudMY/wp3wLj64exsxeRgJ6eiFi/nCm9GrOQmDFo27HkeRlfK4fLA5MA8KxiX63GblOl8q0CuXhIjuvYiPPcp2Uw/yEsxc0SGQZ555l5FhdgKkoDRuC1HWHXjry9g/OpDU4+7IqOFw9C1SN31NyI2P07ONN2JNGmmFVmezhFMcSi3kw3XxbIjNxtdsIKfEwqHUghYP9NsTcnnp9xgAdiflV61fdyyL539zrvc1GxgVHcgDI9qftYrl/jaB3N8mECklchKnzfeqOAmtIOD+3pRsSUNa7Lj28MMY2XLvBRXolUuCZ0AgHokJzB6g5/hvG+jT+wDWMUfYv6w3elM0w0p2E+nekTYik9vEG2zfV8598lMAbEUZJOaU0D3Ik6DhMKJbOMMrp+T7082fOBuWXdU1sKrJZXORDgkOWTXImaPUWYq0F1mY/usOrg5/lMyCy4Cp3D60LWkFZSw9kM7lHf154dpuVQ+WG0IIgQrxZ6bzNOI5NrKlswGoQK9cIjx8/SkrLGDchL9ju8rC08sfoU9cCIbCm3i20308WNKbTwrKGZP6AlfttHBfycu0d3GOk/OfhHbM/GMs8ds7c+2SjZQDXe6Zx+G0QvY9PYbCcivDXlkDwKs3nPuAY+eixG5n58IY2ieUEDK7B/Y3BpNR9BK++hcosU8lVGdjtZsRF9NuKJzK/zY6x7a/vKM/n8/sX+d4NErrogK9cklw93N2wivKzcboaqLfe4fx6PMP8oL3kqk1E110mISIQL4Ke4/5yaVEiRNoKwfrmG98kT2+Lth66fGJKyYxJ4CY1HzcXAx4uOrwNOmZ1j+c3Ul5uBma9yM1cVcsnW2lTPWxE/LFeDSWNLS6xdzvlsGDX7/LhC5tiS0LwytfS/YAMzqTOyn5ZbwxtacK8pcQNZWgcklIOrif7597AgCh0aCx2minb0tk1974eXfFNfU7TNoNlNqvwFP3yWnt66/T/JvBA8dhd2gJ9DByPKuEAW19mNiz8Qccq7e0fWxa9Q6Tw+7n6MbxeMpyuOw+3th0mA5rD9E+HY6GeHM8wI9Bk2/ksil/uySbOV4q1FSCyiXPNyy86nX/SZOJ6NoTo9lMYNSfDyAvA8BQkg2HOsPSf1btn3/NF/zU+9pmznEdHA5IWAt7FsDBHxjiFkR6RCfod4tzMuuKYm6Uu/m/QXfwyrDujB4yFJOnlwrwlzhVolcuGdaKcqzl5Zg8vc6+85zKFhIT3oR+tzVpvurF4QBrKbx4yjAFLl4w5EE4/DOk7ateH9Ibxr8BoX0adIqyg4c4MWUK5isuJ/zDD9WXw0VGlegVBdAbXdAb6zmg1MS3YO1L0H5U02aqPsry4MtragZzcA7A9sez4NWmMrD3dY6j7xFc70NLKUmf8yz5C6snpi5Zt56ilSvxGDMGe2EhWW+/g999957z8MFKy1OBXlFq03em868l2Szw891wZCnYK6DvrRDYFSIugw+HAAJu/Ao6jnVObF1P0mbDlplJ9kfzagR4Q1QUER/PI27kKCwJJ6iIjeX4xEkA5M2fT+T33+HavXtjX6XSDFSgV5QmIKUkMzMTPz8/tNqzj2/isFgo+PlnKuLikGVl5H//A+0f7IA+Y52zpD76OdBoYc9XsOltZ6L+t0P0xAblq3DZMlIeerjWbe2W/kbGiy8CsHBPe4KXfks04D5mDEUrVnDihqm0/fknXDp3btA5lZanAr2iNIEdO3awdKlzbL9rR99E+y5tcPOuWW1UYa9g2pJp5JbnsvD41eR98UWN7XFvxRJ+pRG3STOdE5Vvegv0ZgjpBQPvOqdnB6cGac9rr6Xg559rbM/94suq10Vu4biNGknQs3MoWrECgJxPPyX0laabPERpGuc8eqWiKHUzGp3D0WrsRjZ9lcKCp6snJNmUsok7V9zJmB/GEJcfR255Lna986NojI6ucZyTa31h8QOw4XXocg08fABmLIbB94Oh4T1aDZGRRP26GK2vL8WbNtLmm+qJx/N//RXfe+4GYMTa+xiw62UCZs9G5+1N+3VrAShc/GuDz6m0PNXqRlGawMrPDpFTkMl19wxhy5JYVueswBqdiUM6WJpQPYp3oCmQjNIMHunxEKMWJVBxLBZT375Y09IoWraM9it+Q68rAJ0rBDRelUlFXByJM28FQOvhgeX48RrbNV5eRHz0Ia49e5LxyxLiXn2PItcORJxcRZcjakqJC5FqdaMozaQgq4xFr+ykrMg5HPAnD24ko8tBFrt9hf149WxU7np3HuzzIEsTlpJRmkFCeTIhzz//l6O92WT5NLZvT5svvyBxxgwsCQmnbY/6+Sf0QUFIu51Pd+0mfdJIotP6EvZY7fX7dZFSYpWSUrsDL70KNy2l3iV6IYQW2AmkSCknCCFuAOYA0cAAKWWdRfC/pj3buVSJXrkYrf5yFQfXJaPVnz65/eIu75DqGXfa+mBzMDdH38zN0Tej0zR/IKw4nkDSjBnYsrIoiIjEbrWQN3gYlnHjsEjBgWOxLAyOwmLQ8PDRR/jGpZSPR/xGz8iAMx5XSsnkvfFszi+uWvdDr3YM9XZv6ku6ZDVWif5BIAbwqFw+CFwPfHQOaRWlVSkrtrBn6ccgLWi9HwCgy9AQeo+OIDezmP6+j2PHjtVhxSEdeBm98HXxJcw9rFE7JpUeyCZ3QQzB/x6I1s1w1v2NUW2J+PIL9t1wIzEeXjx+37+cGyrH+aFtFwDcbCUUOJIo07ox7ugWItI78GqncHq7mzDXMlHJLQcSagR5gD2FpSrQt5B6BXohRBgwHngBmA0gpYyp3NbgtIrS2riY9HgEX0NZYQWXXd+OqJ7+eAU6H5Z6BZqI4swl4PMhpaQiLp/S3ZmU7skEIO35bYS+MBShPfuXiLFtW9a//wmfpOXxhYsNLyHRA3qc//p37ECu0ZXJtg/INfhg1xhIKLMwZW88AKLYijaphKR7r6gan35VTuFp5/kuPZcH2gQ22nUr9VffEv1c4FHgXL6O65VWCDELmAUQEXH6T19FuZAJjeDON//WIucu3pRKwZLjoBW4jwjHUWajZEsa+b/E4XXd2ScTAejTLpL8Egfmzu0YWEup2xfYN/Yq/nn0JHqNhqmB3uTa7Ezffxz9nlw0pTYGLt3DjvF92FFQUiNtlKuR42UVrXqSkqNbNlKQmU6PUWNxMbu1dHZOc9ZAL4SYAGRKKXcJIa5syMEbklZKOQ+YB846+oacR1EuVZbUYgqWncDYzhPfm6PRmJw9ZDVGLUVrk9F6GfEYcfaCUx8P56+PfUVldVavCCF4vXPNY/0zMojXLA402eWcNGm4/WACy7MLauxzvKwCgOsCvBp6eReNLT98TU5yEhu+/pzJjz9LZK++LZ2lGurTjn4IMEkIcQL4FhghhJhfz+OfT1pFUc6gIqmQnC8PI3QCnxs7VwV5AI+rIjH18qdwRSK5Pxwj55sjlB3MrvNYXnodZq2GjAprnfvUZkqQNxg0OEKcXxS/ZRVwQ5APR4d2o59HzXb+Pdwb3u7/YjHytrurXi968Rl+fPGZFszN6c4a6KWUj0spw6SUkcA0YLWUcnp9Dn4+aRVFqVvBihNkfbAPJPjd2g2tR80Hr0IIvCd3xBDhTunODMr2ZZEzP4aywzl1HjPAoCPT0rBAH+lq5JpTSuofdW3DE1HBmLValvTtyJ1hflXbAo2td8Lw8K49+L+vFnHNP/8NQMLeXSyZ+3IL56raOfeMFUJcJ4RIxjmQ929CiOWV60OEEEvPnFpRlHNVsjOdotUnMfUKIPDhPhjb1N6YTeg1+N3RHf97ehLyn8FozDoKfk/AXlJ7MA806MloYKAHiDZXD+1w16FEum86RNi6fdx6IIGPk6t/RZTbHQ0+9sVEbzDSvv8gpr84F4CjWzaw7afvWjZTlRrUcFdKuRZYW/n6J+CnWvZJBcadKa2iKOfGcrKIvB9iAfCe0vGsrWo0Bm3VF4HPTZ3J/uwQme/uwe+2buj9a1al+Bv0bC8o5vOUbOxSIoE/u9loBZi1Wly11WVDo0agF4LDJeW1nvv3v9TVp1ZYubBqrptGbsrJqtcbv/0S3/A2tO83sAVzpHrGKspFRedbXXp2lFlrtJW3F1mwJBdhTS3BlleOo8SKo8yGrLDjsNiRFXawS+x5FRStTcbnho41jt3Z7MKvWfk8diy5SfLex6Nx6ugtlmxstmKMxmC0WmOjHLOxSClZ//XnNdb98upz3PfpQlzM5pbJFCrQK8pFw15ixZ5XXXrWmPVYThZRsiuD8iO52POdrVsQoHEzoDXr0Zh0aLyM6IxaNEYtwqBF46LDtZvvacefHRnILSG+CAECgUbAn78XbJXDGJTaHQjhLOlbpMTqkJQ7HGRZbBg1AqNGgwawVg59oEUgBAzyNOPZCEMgWK2FbNhYs3QcEDCO7t3eOe9jN4btP39Pce7pz0Heu+1GZn/7a4vN2qUCvaJcBIrWJ1Ow1DkmTWr39ykK3o7lP69gKAsAnQbXTt4YhoRiCHdDH2xGY2z4R1sIQcAF/sBUozm9t6+UZx/vvzlsXfQtm75zNiq8Yvpt9Jt4PTlpqXz+0CwAdi75if4Tr2+RvKlArygXsNJ9WdiyyyiLyXE2nXCAkM5grLWZMPUOwOuadmhcLo2PslbrwmWDVrFl6yhKir1IT7+cy4e92KJ5slmt/PLqc5zYt7tqndHTg+UfvU3stk1V6zZ8/TnBHToR1rlrs+dRDVOsKBew9Fd3YMtxVtfo/FzxmdaJzHf3og9zw3d6F3ReF1YddUtwOBz85z//ASAyMhIPDw/CwsLo1q0bJlPTtd2XUnJs6yaWzH2pal1kzz41Ar65XXuuumE6IZ2imf/4Q9htNm5+4Q3MXo0//+6ZBjVTE48oygXMe2ondH6uANiyy8j55ggA7leEqyBfyeGobraZkpJCbGwsRcU3seDrGWzfvr1RziGlRDocWMpKyTgex+rPPuKNaRNrBPmpz7zI8JmzaqQriY/DKygYo8nM+AceobyoiO+efZzSwoK/nqJJqRK9olzgpJRUxBdQtPYk1rQSTH0C8BzXtsUe7F2IYmJiWHjKROdDhi4gKak7J5N6MGfOnKr1WxfNZ/MP3yMddoI7dMLk6YUQAiE0CI3G+bryX5vFQkVZKdayMgpzsrBbrZQXF9V6frO3D7rSMgoqyk7bds+8+Zg8vQBIPnyQRf99mtDorlz/+Bw0msZ7vnCmEr0K9IqitAonTpzg888/r7GuW0QYnSIj6DhoCJayMubdO7PGdv/IKKTD4fyrLLVL6Xyt0xswuLpicDVRWpBPVuLpE7QAlLsLenQdQv66tfjpXRjw5tt8/o97AXjwq0XoDDV/ee3/Yxkr573L4Kk3c9nkmxrt+lWgVxTlkmC329m9ezd/6N0wennj9dYcclOr+wVoXUzYy0urlv+xcEm9j/3BrOmUFuRXrxCCNoMG0O3myXT274K0WBAGZ6sgKWWdv7iklCx95zWObtnAjNfewzc0vGEXWQdVR68oFxkpJVZrw4cjuNRptVr69+/PY72ieTgyiMlP/AefUwLpqUEenPe5vu7+6Ksay/fMm8+Uh56is79zcpY/gzyceZ4OIQRDp92CdDhIPnyw3uc/H5dGmyxFuQhYLBZ27dqFxWJhzZo1AFx11VX0798fnU59VM+Fh38At77xATaLhSOb17P8g7lV27yDQ9i66FvaD7gMv/A2OEpKcJSWog+ofZIYIQSzv/2VX9+cS/SwwZg8PM85X6UFzoexBlfXcz5GQ6iqG0W5QBw+fJjvvjt9EKzJkyfTvXv3FshR61ReXEzs9s3EbFjDycMHAHD388fzWDw+5Vb6fPsdPsGhCE3TVHiU5Ofx29uvkpkQzx3v/A8Xt8aZqKSx5oxVFKUJderUiSFDhrB58+YaVQqRkZEtl6lWyMXNje4jxtB9xBiKc3M4vmcnCXt2crKggGSrhf2z78HgaiIwqj1B7Trg36Ytbj6+GFxcKczOJC3uGLaKCnRGI3qDEZ3RiMHFBTcfX9x9/XH388fF7FZVfWMtLyflWAzJhw+SHHOQtNijSIeDEbfe1WhB/mxUiV5RLjAOh4O8vDzy8vIwm80EBwe3dJYuCdLhICc5ifT4WNLjj5EeH0tW4gkcdluN/bQ6HXqjC9aKcuw2W63H0htdMHl6UlFWRkVxMVI6EBoNgVHtCe/Sne4jr8I7KKRR869a3SiKopwDm9VKQUYaxXm52CwVGFxNhHSMRlv5zMRht2OzVGApK6M4N4fCnCyKsrMpysmktKAAg6sJk6cnIR06E9IpGoNr0/XUbZSqGyGEFtgJpEgpJwghbgDmANHAACnlaZFZCBEOfAkEAQ5gnpTyrYZfgqIoSvPT6fX4hkXgG1b7vLsarRaDqwmDqwk3H1+C2nesdb+W1pCnDQ8CMacsHwSuB9afIY0N+IeUMhoYBNwnhOjS4FwqiqIo56xegV4IEQaMBz75c52UMkZKefRM6aSUaVLK3ZWvi3B+UYSee3YVRVGUhqpviX4u8CjO6pdzIoSIBHoD2871GIqiKErDnTXQCyEmAJlSyl3nehIhhBuwCHhISllYxz6zhBA7hRA7s7KyzvVUiqIoyl/Up0Q/BJgkhDgBfAuMEELMr+8JhBB6nEF+gZTyx7r2k1LOk1L2k1L28/f3r+/hFUVRlLM4a6CXUj4upQyTUkYC04DVUsrp9Tm4cPYY+B8QI6V847xyqiiKopyTc+7jK4S4TgiRDFwG/CaEWF65PkQIsbRytyHALTh/Beyt/Bt33rlWFEVR6k11mFIURWkFLrqesUKILCCxGU7lB2Q3w3kuRure1E3dm9qp+1K35rg3baSUtT7gvCADfXMRQuys6xvwUqfuTd3Uvamdui91a+l7oyYeURRFaeVUoFcURWnlLvVAP6+lM3ABU/embure1E7dl7q16L25pOvoFUVRLgWXeoleURSl1VOBXlEUpZVr9YFeCLHwlF65J4QQeyvXjxZC7BJCHKj8d0Qd6X2EECuFELGV/3o36wU0oTPcG18hxBohRLEQ4t0zpJ8jhEhpbb2eG+G+XHLvmcptjwsh4oQQR4UQV9WRvlW+Z6BR7k2TvW8uqTp6IcTrQIGU8j9CiN5AhpQyVQjRDVgupTxtrHwhxCtArpTyJSHEY4C3lPJfzZz1JveXe2PGOaR0N6CblPL+OtLMAYqllK81X06b1znel0vxPdMF+AYYAIQAq4COUkr7X9LMoZW/Z+Cc702TvW9afYn+T5UDrE3FecORUu6RUqZWbj4EuAghjLUkvQb4ovL1F8C1TZzVZlfLvSmRUm4Eyls0Yy3sPO7LJfeewXnN30opK6SUCUAczsB2yTmPe9Nk75tLJtADw3CW4GNr2TYZ2COlrKhlW6CUMg2cM2YBAU2Yx5ZypntzNvcLIfYLIT5tTVUUlc71vlyK75lQ4OQp25Opeza51vyegXO/N032vmkVgV4IsUoIcbCWv2tO2e0mqr9hT03bFXgZuKu58tuczufe1MMHQDugF5AGvH7+OW4eTXxfLmrneG9ELYeqrV74on3PQJPfmyaja86TNRUp5agzbRdC6HBOZN73L+vDgJ+Av0sp4+tIniGECJZSpgkhgoHMxshzcznXe1PPY2eccpyPgSUNzmALacr7wqX5nkkGwk9ZDgNS+YuL+T0DTXtvaML3Taso0dfDKOCIlDL5zxVCCC/gN+BxKeWmM6RdDMyofD0D+KWpMtlCTrs39VX5ZvzTdcDBRstVyzvn+8Kl+Z5ZDEwTQhiFEG2BDsD2vyZs5e8ZOI97Q1O+b6SUrf4P+By4+y/r/g2UAHtP+Quo3PYJ0K/ytS/wBxBb+a9PS19PU9+byvUngFygGGeJpEst9+Yr4ACwv/JNGtzS13OB3JdL9T3zJBAPHAWuPmX9JfGeaYR702Tvm0uqeaWiKMql6FKpulEURblkqUCvKIrSyqlAryiK0sqpQK8oitLKqUCvKIrSyqlAryiK0sqpQK8oitLK/T/qnSb3zej+AQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS in case we need to triage them\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy  #uncomment to plot all precincts\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e8b76b0f-c7ef-442f-ac2d-31672731c8d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now find low-pop tracts -- these may be ocean or lake tracts\n",
      "there are 16 tracts with population less than 5 .  They are ...\n",
      "[59, 164, 166, 167, 475, 832, 972, 1012, 1303, 1312, 1319, 1329, 1473, 1555, 1556, 1616]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now find low-pop tracts -- these may be ocean or lake tracts\")\n",
    "isSkippedTract = [0]*nTracts\n",
    "isSkippedPrecinct = [0]*nPrecincts\n",
    "\n",
    "minTractPop = 5\n",
    "skipList = []\n",
    "for t in range(nTracts):\n",
    "    if tractPop[t] < minTractPop:\n",
    "        skipList.append(t)\n",
    "        plotPoly(tractGeom[t])\n",
    "        if tractPop[t] < 2:  #definitely an unoccupied tract\n",
    "            plotCenter(t+0.1*tractPop[t],tractGeom[t])\n",
    "nSkips = len(skipList)\n",
    "print(\"there are\",nSkips,\"tracts with population less than\",minTractPop,\".  They are ...\")\n",
    "print(skipList)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fec6f540-305d-4a24-b1d5-41db297dff39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the lakeshore tracts we will truly skip in building the map\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the lakeshore tracts we will truly skip in building the map\")\n",
    "skipList = [59,166,475,832, 972,1329,1473]  #for MA, all others are interior, should be kept in map\n",
    "isLakeTract = [0]*nTracts\n",
    "for s in skipList:  #confirming we \n",
    "    plotPoly(tractGeom[s])\n",
    "    plotCenter(s,tractGeom[s])\n",
    "    isLakeTract[s] = 1\n",
    "    isSkippedTract[s] = 1\n",
    "plt.show()\n",
    "nSkips = len(skipList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac6f8f18-8067-4078-bd95-006f5a5e89df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 500\n",
      "working on tract 1000\n",
      "working on tract 1500\n"
     ]
    },
    {
     "data": {
      "image/png": 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PlKFIGYpo1EzY4HQYPdZCNKeTvIo4khI7/y1DmEyEXHMZ8X+8lJDUTLxaDHptTae32x2w9Ilh4OIPUCP74M3PCYgMep3vb2wLPnVzMetwJl889TcyDqZ3tVhtYvagAUwqzwPg9m0H2HwwcBvtrWHEjJH85KVLGXeeHSk9RPepwh56/O8RFhvO1Y9eyg1/uggpPVhsZdRWGMEFWouxCdxCCjdvxittJA2P69J2zYkRkKniTT+IZXTvCZ6tlR5BrykLSNvSrwC2/uBaPFYrusmMbragW8wM3L6FgYDz5X/x+dgJhF97LWcvmIfJ1D3/K4UE2fji8vm8tG4Tfw0O5bKMUn6cdpBH5p2HyWwOtHinRVEUzr5yCmdf2XSeisIyhDDjdkby7h93MeeHCQydcmaaS3cGxgygheRsOwzo9JnWsbFRm8NbXA3omFJSurTdQKLrOpiDka7AzHrMoT7rE2doGK7wCDSrFaF5MVdVkZuUTHZKP9KuvZGojMPEPPgAa2eey+ePP0NhN94zuGPqJJZNHMwAZw0vBicw7+NvSMvo2W6YP/jLp3z0xP4T7gUbJqGtonu+tnRDcrIksfZ8bFGNx0/tLNyFXkymQpSwmV3abiBxH8wBTy2oLfWq2rGcf/stVP/gCoZFRZ42n/uhB1jz2VfUvvcug/73Hwpff4V1U88h6YbrGT/j7GO+d7oL/aMiWXbRufxlzUb+GZXIhftyuX/vPn50wTyElFi76SymMT56/DOKjoSimsrRvD4vouff1oe+w7qHx+CeQs/5iwcQd1UlhTUJjB2c16XtSilx10Rgiyju0nYDhe7VOHLL/Th3rQfAPqlrle1RTCYTEc0M/uA7OzD76svg6ss4sHsf+19/g6Ql3xK8ejnL+qaQ/NRTDB7TvQ7wKULwq+lnMb+giPn78vgTDh5fugWL5uGD4cmMT+l7Spl1B9MRQjBlYP8ASHwqe1ftpiAjBKjhhj+ej1B8rtCDHGe+sURHYyiAFpC3diM6JpLGdO3bhV5QiK6HYUmo6tJ2A4GuaeT/6lnqNy1GWEIIvfwnRN95daDFajGDRw5j8BN/obr6YZa99AqDX/4XR9Zv6HYK4CijYyJhn++FxmO24DFbuGfHAW7PL+BIdS05Thd5XkmuYiY/NAqzx8XaUAd9Y2O7XNb66loWv/w9UX3CmHzxZEpzfXtDM65JwRFhnANoD4YCaAE5u3NQSSBhyqm+/zsT9969gBnzgOaibfZ8nLsPUfXpfwFI/eRTbAN65snnkJAQYiZOgJchtG/3/buZVBP3mZ086/GZut5UV8jrIXE8VA+YwlGDvITX1xDtcZJUVcym0Bh+sGorixfMxGHrWsdyy15bQU6anZw0D/vXfY5QQMpw+gzpvv3bUzAUQAvIybESH1qAKbhrHU+5MwqBBMwjut41Qldj7e/7zxw8+4oeO/gfpTI7mwggNvnU5ZTuxIPTpzA3M4vi2jrmjZjH3D370CUMio0mJSoSVT0eS+HFdZv4Q2Q8N339PR9fPv80tXYs9TV1ZO+v52g0M1e9z7Fw8rA6IhNOcTJs0EoMBdAMdfn5lDrjOWtwbpe37SnUMZsKUEJndXnbXU3GD+4CwJNzJMCStJ+6HN/SSkJq97fcGt9AxrkjGjef1KTkgsED+H79NlZFJLIrK4tRXWCVVllUwft//g7Ne+J+jMlSxvx7Lu309nsDhgJohtz1mwAHSRMGdWm7xzaAI/Obz9zDkVLiObwJANHNbdNbgszPpcoRitVxZpgk3rl0NV+oIWD3vXEvO3i4UxRA/qFcVr6zkbJ8MFk03E4LEInVVsrwcxLZtzaPkEgT8358Xrc9d9HTMHqxGXL2lmARCrHjzunSdrX8AjTpwJLYfQN5dBRCCLDYsY2cSsr/ng20OO3GXFBAVUzXb5Z2BpklJXwjfcsvDo+L5LpKfji34/fCMndl8NU/MoAwkE7cTl+b06+KYMyccwE4+4oOb7bXYyiAZsgpcJAYWYRi6do309L9n3DgvOewuGIJ+XoNoaFjiEiYTFjySBTzmfdnUyPi0UqKUaw9fwYQXFyEMyX12O8j6Rlsf/UNJv3wRhIG9GPL2g1oThejpk4mqJv7G/rDms1ojli+TglnfP/UTmlj3cdr2LrYBYA1qJRbn7ycpa8tIzIhnDFzes/p90Bw5o0kHUhV+kGqPNGMGah3eds1rABFwyQclKurKHUvIiMLlPQg7K7BhJhHEhY5nvgx81CtPX+WYI7rg/vw/uYzdnM8mk5USTE5U3wuLQuLS8i75hoGVVeR//knbOs/kH77dgOw32whd8gwvLPP5aK7foTazQ6OAaRr0LemjPH9x3daG/vX5YG0c8Edg0kZPQPFpDL3trmd1p7Bcbrfv7huRM76HQAkTer6IBq65rP9D1WqmTl3O5OGfsVAx2+IZA5etYp889vsr/k5h9Y91+WydQbmxL7oNSXodc5Ai9Iu8goLsXncWPr4LJlW/v0fhFVXkfvHv5A5dgL2kmIyrvgBxX99gqwLF2IvK2XI88+ya836AEveOBWKiUjZuS9AmgcUk4sB4wcZa/tdjNHbpyE7rQq7qhAxfFaXtz1gxnscWTMOVbGhKAqhiUMJTRxKCjcD4HHXsGbZNMq01V0uW2dgHT6U6kWSgj+9SOJf7g+0OG2mNDefIMDy0Yd8ciidkYu+pCIikvOuugyuuuzEzJddRH5hMWWzZpK/cjVjz2k8HnIgqTVZKfG6eOb7VXhcLm6cOJbE6PbHLKutrGH1e2tRVIGzPgRHmOHJMxC0eAYghFCFENuEEF/6fz8phNgvhNgphPhECH+stlPLZQohdgkhtgshNje4HymEWCKEOOj/jmj303Qg0quRWxJJUmxFp4R/bA53eRoAwY6hjaabLQ40Uz11QfvR3D37rRkg6tbLsQ47m8qP/0Pxfz4MtDhtZujI4aRddxO1kVH0Xb4UgOqQsCbzJ8TFcKRPMmLTxq4SsVVcoteSExbNE4TwrDWaZzZsbVd9uldj1bsrefu3izi01cSBTSpCWJhxrbHWHwhaM7LdC+xr8HsJMFJKORo4ADx8mrKzpZRjpZQTG9x7CFgqpRwELPX/7jaU7t7lC/84JDwg7dfmLwPAETOlyTxhrimY3BGolu69kdgSFJNKyut/B6Dk2T/gzu2Z/o/sQTYu/c3DLPzoPcZt2YT64ccM+ve/msxfXVVNVGkJXnv39GPz9MUX8GnfEP4d43PMl1VW3q761n2yjp3Lvbidx237k4bU0X9czwhWc6bRIgUghEgCFgAvH70npVwspfT6f64HWnsu+xLgNf/1a8ClrSzfqeRs3gt0bvjH01FT7nvTcvRp2iGaS+Rj83b/w0YtpezdbwAwJQ7CFNOtJoRtQlVVBo8cRr/Upn1IrXz1DULqa0m4844ulKx1TBk4gFn9UxG6Tpa96dlMS6gpPzGgkqJUcsl9C9tVp0HbaekM4Dngl0BTu0G3At80kSaBxUKILUKI2xvcj5NS5gP4vxs1nBZC3C6E2CyE2Fxc3HVvhTnpXRP+sSlq6zOweBTM9oQm85gJx8uZ4Siu9NXPKHnmd5gShtDvg1dRLGf+9pTb5Sb0vXfIGDKcCTOmBVqc07I5Nx+pKCTo3uYznwZ76NHZag0X3N6X255d0H7hDNpMswpACLEQKJJSbmki/VHAC7zVRBXTpJTjgQuBnwghZrRGQCnlv6WUE6WUE2NiYlpTtM1obhe5FXEkJQQuyHSNLMNB+GnzWJV43OZCyjO2oWta1wjWCZS+/RVFT/wKNTaV1A9exdRLPDwuf+8joktLCLn11kCLclqklDyxxxdK8p6RbTsRv/6zdbz7+4/Z8X09AJfeN44B4wdhsQYm5oOBj5a8Zk0DLhZCzMfnkSlUCPGmlPIGIcTNwEJgjpRSNlZYSpnn/y4SQnwCTAZWAoVCiAQpZb4QIgEo6ogH6ggKN27yhX8c0bXhH48iawqpteokWVJPmy80dAwlzq/ZmnEl6oEQHJ5RhIecRXTKLEL7DkdRu7+Vb/2BIxT96SHUqCT6vfca5ujwQIvUJWi6jnjjNfL69GX2wgsCLc5pOVhYxPawOKZW5DN39oWtKluWV8I7f1gHBAPhCOG7LxTR4XIatJ5mRwgp5cNSyiQpZSpwDbDMP/hfADwIXCylbPRVWQgRLIQIOXoNnA/s9id/Dn6bRt/3Z+16kg4kZ7s//OPUSQFpv/rI1+iqIDj89F5A+539IyaPWEx/+68I08+iTj1AlvYsWw5fwqrFZ7H1ix+Tueo1vHWBm8k0h15WCbqX8B/cgDm+93h3zM/JIyk7CwmkdfNA7XaTiqJpKI2/451CWX4p9TW+f3OL/r0C3+APIRFlXPTT/oyaaSJxkOHKuTvQnoXWFwArsET41Pp6KeWdQohE4GUp5XwgDvjEn24C3pZSLvKXfwx4XwhxG3AEuKodsnQoOVmSmKACbDGBiUhVUrwEgNA+zb8ZhsQNICRuAHALUkpqCg9SnLmMcvc6qqwbKfcsI339Hxge8zwJo7rOjW9LsY4YAAjcGVmBFqVLSUpOYuUf/kLMH35L+mNPMuzVl5svFCCSoqO51LmVjyMS+XTLNi6d0LhhxMbP1+OsdbNreQ0IG1JKhIjAZi/l8l/OwR4ajNVuJXlEatc+gEGTtEoBSCmXA8v9143abfmXfOb7rw8Djb7GSilLgTmtab8rcFdVUlidwJguDv/YkNr6DCwCHNETm8/cACEEIfGDCYkfDNyJlDprFs3GZc0h/cjjWIOiqSreQ03VfpzeXPoP/RmRA1rXRkdjCrEjgqPwHOldCgBg1AXnkfO7XyNSA2No0BoemzODZcs28dvyeiIOvM7Uy67EYjtuunpwUxqbvvbPNIVvo9f/4secH04gIr75EJsGXc+Zb2rRSvLX+8M/jg5cMI8aWUqYHt7ueoRQSIm+iwPVj+IKymHbkWt9CWbfp2DbsIArAABTVAKewpxAi9HlHNi8lVBdI3zc2ECL0iyhQTYejQ/h60Wr2LxzLTm7t3P9n5/BWVvLW48+jdN53ENodJ9Krnr0EpzVvg1fe9iZ4Rb7TMRQACeRszMHhUQSpgTmWL5WX0KdxUuc6JgA3H0nXYPcKKmpOkBI+FBC40aAW2Vz1kJUrW1O5HRdp740m6q8XdRVZdN33LVYHOFtqsudU4DnyC6ErX325T2Rki3bCAUGnhV4JdwSbhg/hi1ffQpAwaEDfPDHR8k/mIYS9ONjm7sAJblhlOeXEtWna6z2DNqOoQBOIifXQkJoAWZHYEwRa3KWgBCERHSc98Xkydee8Lsiy+fkLqh/02cMjlJflUt1bhpVJXuorTlAnX4Yp/kIuun4xrLc6mXgjJ+2Wi4pJfm/fhqAkHndZguo69i1k4LYeIbFB8barLUIIXC4jrsdObJ7B4q5PwomLLZybA6oKvEd4HvnD1sZNsXEnFu63SqvQQMMBdCA+sICSuoTOGtg14d/PEp1sc+5myPx3E5rw+P0HR4zW06McaxpTqoL91ORtZ3Ksq1Uia24bccjkimKHZuWQqQ2G0fQEEKihrKr4HZqa9NOqKe+vIDK3J1Ul+6jtu4gNfouBBYmn/sp5qDjywH5v36GunVfIoIiSPjLfZ32vN0RKSXR+/eSNXZCoEVpMbtz8whL3wOzLuSGCy7g4Ib1bFtqR1EquOFPF/LpU4uO5RXCzP4NMHBSBikju/8eR2/FUAANyFm7CQju8vCPDamp3otJSGwxnWeC6nX5PC9WFO2kavleap1p1MnDuMx5oPgOlClmGw7PKOL0y3BEDyGsz2iCopJQTnKMZz88jBL7N+xa9HPqvOnUm7LQLMdPJ6uKA81aA0DO1nfpN+22Y2nViz8HoN+nH/WIMwsdge7xsO6rRRQvX8GQqkoYNSrQIrWYz77+CpuUXLLgIuKSk8naVYyi1jF6tpkgh53YlDDKCk4s43F6AiOsQYswFEADcvYVYxGC2PFdG/6xIdVaPg4cneqBVPcf589TXwEdzHoMNi2VCKbjcAwhNGk0oX2Ho5qaj84VYh5BHfsoVr/BpiUTJqfgUAYTEjWM0D6jsIUnIDU3y1eNoDD/C/rhUwCuI/noVUWIIAfWlOaXos4EMtIOcuiBX5B0MA27xcqB8ZOYcHHP8IOzNysL77rvqUsdzMBkn2+j/PQSwH5skJ9zyxwGTDiMI8JB2voDqBaVgRMHB1Bqg+YwFEADcvIdJEYUd1n4R29dEYolFMXkM5vT3TXUWNwk0bmeEeOGzUXb8UeCQpIITRyFNaztjteGzf49qRV3YI9KQVHVxjMpNoRuxm0vPHar7PWPAQia1HsiP6Xd/3NS0g+y89zzufSZxxln6/5eXB/75z/Q1y7D6nbhAMbd/fNjaePOH0lO2mH2rHERHreZcfMmkjraZ7wQ3ffMiIl8ptM75t0toOrwIao80SQN7JrwipqznDWrp7B8+XC2fT2WzOXXUrDl1+iKICS888LvAZisQSRPvo6YYTPaNfgDqBYbjtj+TQ/+R9usj8RjK0Lz+DYR1ZBwAOpWftKu9nsS4fffj1dVGb1sMd+/8W6gxWmWQwWFmJd/g9Xti9dbPmYK5048vmeRPCKVvkPrEMLKmo9L8Xrb5yjOoOsxZgB+ctZvByJJmjy8S9qryVmM1ySwusFFHen6RvCZTRPa58x7K/YE+97+peYFM1gH+1wBiJM2os9kovskUhQSRkRFGeGp3c+N96HcXCrr6pkwyDcDXb52zbE0z7kXc88lV5xS5uKfLeTFuz5FSvsp4Ry9Xp2C9EqkLpFSoqiCxEHhp+wjnUxdUTmHv9qM1+XB4/SiuXz7UqNvmYU9tue7Ce9OGArAT05aFXZVJbKLwj8qZp81zODom4id+FvcFQepyHgfzV2JPSFwexCdRbR7PiWWrylOW0XCmAsRZv/yh2iZf5meTklxCWXXXUdEXS11L7/ClOlTAy3SCeSVlPD+Q/didTtZv+AqfnLTzVw97wL+894rAARvDufDrVu4+5/noSgKO7/fwZoP0rGH6pit4HaaqC6rIiQy9FidH/xlE2V5tSe0kzo6mgV3j25SDs3j5eNHFlFpigNOnFVGfL+TIT+Y2XEPbWAoAACp6+SURNI3pqzLwj+aQ3ymcR6nz2zCEj6I2HGPdknbgWDojN+wev3X5B54n4QxF2Lp53sDto7o3n7wO4rKyipC6mpJHz+Jhd1o8P/XZ59RsGMr5kN7sflDi9Ys/gxuupm03OPm0IrqiwP8z7uXc9ZFIRzZk4+uh1NTcbyuwsMFJyiA6lInqklh6NnxgGDPylzqa9ynlWfTM19QaYpjwoBK+p87Amd5LV98WE6Ip5iBl586AzFoH8YeAFC2eyf1WihJQ7ruNKo5JBUAj6uky9oMJFa771SobvUdIBP+f3nO/avZO3Ushx77caBE63S2rl5P/o034lFVhv36V4EW5xgbdu6k9u3/ELJnC/Upg4i+6XoATB43T/9gISt/fR8ui5VxD/6R1JHHD4Bt+KKa/MOnLt19+/J+nLXH83lcGqExQcy6biizrhviu3maCV95WjbbD1mJ0fOZ/PNLiJ0wmM0f70HRPcy7Ywyq2Xhf7WiMHgWyN+4D4ro0/KNqDkbVwKOdPsbqnr3vkpX5DkIEoaqhmM3hWCzhxMVNZuDAwHgrbQu67gsmV+3YDIAzO9OXUFeHqAPPq6u7WVTojiPrjTcZWl5G+o/uZOGw7mMWme6PsJd04x3MH9qHd//8MKEct4AzX3wNV8+dR9/YGLwjR7H0tWUc2nLikNF3aD3Z+71ACOCg4HA+qaP6UVHoU/RhMSdaOkm9aQ3w/XMrkCKGc++ZiqIo7H71O/L1REb3KSZu8ryOeWiDEzBmAEDOYSdhlmJCUjvG/05LMesKbq3pkI5btvyT/PxfoajpCGUviOXo8iOcrv+SdeQOsrLWdqG07UNRFJA+hzGa20lG8avH067wHYYq3baosaI9ngm/fZT8+D4MePlfLH/l9UbzuJ0u6ioqkFJSVVV9TGF2JpfN9AXny3njJb5+9DfY3Cpj7r4JXUiqB9j5v+tvoG+sb+ZmspiY9+Pzueinx/+PjJqpctG985l7yxDyHAeotBTxbHYO1y1ZQ/b+UgCik050qdJUSIE9ry8lXyYyMqmK6NH9qc0rZd3qOkI9hZz94GWd8PQGYMwA0Nwu8spjGZxc2HzmDsYsrVRSgrNoM7bYEx2CrVv/LLW1/8Dp7MvMGR8QEuJbg3W5aliz9jdI+Rm79/yclJR1XS53WzHXxeEJLiB3xyeURizB9nQ/pl64mCOvPEw9u6hY/jlR47p3dKy2kJyYQPiH75I7/Ry8n3wMt96E7vWy8uPPqPnqa4KPZBFTmI+q61QFOwitreHwbXew4Bc/a1N7Xk1j597llO/6lPiSXYDEOve3DBg0HWE6HoIxyGQmOzmVvkcyAZjz0C84sHktihSMnn3+CXXuWr6Dle9mIqUdIVQkLsL+8hv2/76G5TeM5/NRO5G6Ss3KP+MeHUGklsUwIL5/6An1NBY4sL6kinUrqnDgYeovLwFg6WOL8CixXHhdMqrNCBvZWfR6BVC0eQseGUTS8K4/uNK/z23sLnietTuvZmjQAhKnPQ/AihV/xuN9Bbe7P3PO/ZCgoON7E1arg+nT/sSS79aiKLVNVd0tUYUND5BR8hRYISHsKhRFwTHyLOr5HMVub7aOnkpodDTLLrqMIV98wtJ/vYz7yy9JPZSGEhlN6YBBVJwzE4/ZTFBJCaHffoV2IK35ShsgdZ2MAysp2foO/TKXMN5diluY8SgmgrV6eO8yatUgjlz1AcOG+qzMluTt4bsxO4hJFvzq/CdwV5eTv2gtXgtcMP0HJ9SfvS8fCEEI8FjqMLvtrJ/8W85e/yv2lG+Hvgq2qmHUAMKpUWUuAiJIGBjerOwrH/8ClymO8y4Nw2Szkvb+KrLdCQyPLiBp1plnEt2d6PUKIGdrOpBA0tmTu7zt6FH3MTliDNt23koaX5Hz1WKcOniDvXg9g5lz7gdYradutlksdlR1CEJs7nKZ24quaTjtmQB4rRVYnUkkT7sJAMfASRQD3vJSXBX5VGxZTNXmldSv24gSFMywN1ehql1zOrszmfG7X7Fh5w5Sn3uaOlsQ2Q8+yuwbrsHcYHNTSsn6yavx2Fp3IHHldy8wc+2vSVCs7E44h+zhlzBi/MUEB4VzJHs3WTs+ZermZ3Av+S3lie8TERrNbzc8jdAquPmC55k9YDJ/uucyggRc+qvfYw86celm5nXTydixEQCz26eopYCD8R62DfAt7a2o+Ia1Y3ZRGBeOc+lNyKAYLLYTh5iTJwDZy7ZzqCqOfsEFpF54HvWlVaz+thSHdDH9kUtb1QcGrcdQAFl6QMM/2pPOZYL5A3Zuvp7qIN9JyprqSGZNf7nRwf8oihKEED3H0ZbbedzayeJK5OwLlqEoPjtvW3RfpCLxvrqew68e94LqG/IrEfLMCCAeGWxn8AvPs/OFfzD2rjuYMPTUDeGtu/cRXl1J3eDWOSRUzVYADv3gEyYNOdG0NrnvSJL7jmRv5veMKdnMgf8uIOK+Ddw98kcs/08G+9c/yfNDPyW4VCP6ihkMG3JqfIKVr68ATlRKdns1/7pkCFXBB3mqsJhgKSlY9wt0ayLj5gxm3HmNBFVqoAE0l4flb+3HQhCzHloAwPd//QqnKYaFlyZgdpy5M8LuQq9WAJ7qKgqqExgzKHDhHwFscZOYvOAAmrOMtW9dwZ7CiZw/J/S0ZVTVDmhomhtV7f5rpLbgOPpbf0VZ+QrGznvp2OB/FPNNZ+NatAnT2YMJmTKTyCkXkX7xAtQKSdm2r4iedEmAJO9YBgzqz4C/Pd1kevr7HzJcCCZc2bqNz6Ej56GveJCavV/BkMbPVgy642s2v/1jJmZ8xo5d33Hwi3UMywpGFxLv2kPUJlr5v8sbd8ttDjo+A7vwSokE+s+6mjGvSVavi+UQCukqzLttBGFKPtZwDcV0qo1JWX4tr/xiFSDw1jnxmGOZMcmDPTaC7e+sI6MmjoGOXFIu7DkWbj2ZXq0A8tb5wz+OCVz4x4aotkiEaTZut45qsZ4+rxKMpoPLVYXdHt1hMui6l9WrH6W6ZjepKbcyYkTHHb7pN+0W+nFLo2mDHnrlFDNQdWp/+Cadwv/+/YxRAKfD49VIWLqYI2PGMzIhvlVlo2NS2RYzmdQDHyH1PyKUU30zmc1WBl3xN3jqM/T/3ENo4UDEnIuYNDGJHRuW8Ysf/gGzqfGXifELJpC2eQdmfSf9z/vZsftjbruO6IHL+fQdneF9Mildm8Oi3UlACTc+KAntN+BY3phkB+UFdXhcGl63Dig4ZCUjbrkEb52TNctrQShEz5rSqmc3aDu92gw0Z1c2Ch4SpnT9+n9TeDWf+d/JflVORjX5XEm4XJUd1nZtbSmLvl2Ix/shVut+jmT/Dk3TOqz+1qKE+JbA4m/rHcFiNi9bQXRZKfZL2qbsakddS0J9AQf3fNdknjBHBHuv+YKMGl+Q9vFFG5k1fgH33vU0QUGNxO6Vkrq8Q3z5qxtxlj/DiH67T8mSMG06w/tmsudIKpt2J6HiBhTK0g6fkO/qRyZzx99n8aNnZxAhSzB567jk/vEoisK6p744djrw8O6KNj2/Qetp8QxACKECm4FcKeVCIcSTwEWAG0gHbpFSVpxUpi/wOhAP6MC/pZR/86f9DvgxUOzP/oiU8ut2PU0ryckxEx8SuPCPjaFpOgqyWYdZZlMwbjc4nR2nADZs/BMWy0Eslh8h9RqEeJfq6lzCw5M7rI2Wous6+vs7kIokalLP8JnfUnRNY8eiJez5+lsKHSGMGjwAmZdH8lu+MwITL17QpnrHTLqKuuUPUbjzUwaPavrg1PChM0h84Ele+8Of2LKrkFG71xA18sRlo8zlH7P0f/+hxg1eXQUUglQPk2471V2JYjIx+9FbGbxiBYfWpjPu6um88WQOK78oJWlGHaYG1l21eSWsen455SKaqcPKCB+SQvm+LHZnhxBvyqM8KJmSnJo2Pb9B62nNEtC9wD7g6OL0EuBhKaVXCPE48DDw4EllvMDPpZRbhRAhwBYhxBIp5V5/+rNSyqfaIX+bqS8spKQ+kcmjAhf+sTG8mo56uvPygKZ5qKj8ElVVsFjCO6RdKSVO52qkTGHypFtYu/ZCJEEEBUV2SP1tRehnxgbwUdL3HSDrnntIyM1miC2ICc76E9IPpw5gWHDbNj9DghykRQ4jonhns3nDh0/jhvvu4I1n/82rf/wrsbHh3PDs//C6avnswZvIKtYBlcRIlcpajf5JDub89hVUa9Oy9Zk5kz4zZ7L6908B4/FKM9762mMKIH/dHr58+RBucyRx7gzG3nszAMv/vhKIYfY901m/qoqM7SXkpJWRNCSw//Z6Ay1SAEKIJGAB8GfgfgAp5eIGWdYDV55cTkqZD+T7r6uFEPuAPsDek/N2NbnrNgLB9B3fucFXWsv+Ijc6p9/UXbHip5jNh1DELURFdczp5SNH1mK1lmGzXs7atQ9iMleTmPDcaS2ROpOGMyBPbQXm4PCAyNHR7PrvK6QUFXL4kV8z86rLKTicSXpGFuOmT8FSW0vEaVwlnI5DaauoyN1BkDAzsHIPLo8Tq/n0AWeizrqU8+Zt49tFWygqqmD/238h9cJb/YM//Oj3vyZs6FmtkmPFX//L7nxfPIvrfz0ea1TMsbRN72zHbYrjnHFOhl5zNYp/mTNPT2BAaCGRI1IZbSojY3sJO7/PMRRAF9DSGcBzwC/xOfxojFuB905XgRAiFRgHbGhw+x4hxE34lpZ+LqU8xTGOEOJ24HaA5OSOW4rI2VuMWSjETug+rpellDh1Batoet19w4bnkCzB5TqHCy94FK/XzeYtr1JZuQePJxMh8hDEM3/+F61q+1D6O0gp6Nt3LmkHXkHXpjJ8eNcvveTkbGPnzmdR0vdw1GO+7qqDM0QBhG7ZTPaIUVx803UADBwxlIEjhvoSw9vujFD/6udMrPIdHtMRlNdWYg1vPuLYyFt+T2i/D/ngn69SlJ5G/TuPAzBtckqrB3+A9JwoAK65KwJrwonB4LPdCSAgYUIqljDfULLntSUgVCITfLOEpCGRqCZB9t4ydF1vdinUoH0027tCiIVAkZRySxPpj+Jb6nnrNHU4gI+An0kpjzq/+ScwABiLb5bQqG2clPLfUsqJUsqJMTExjWVpE9n5DvpEFHVZ+MeWUJ2XTj02Zg5t2qqnrPx/AIwe9VOEEKxY+TNqax/HZPoSVc0GXFise1n07UhWrf5Di9rVdR23ew0uZ39ycpeiKDqDB7fdO6fLVUN29ga2bX+V4uLTn2gtKcng++UPsujbUSz6dgw7d92OWVtPwjtOdJMg/O+/xBqZ2GZZuhO5R7JJyM9FTG79wNocHnMwJeYIMn+0nuJ7dhIVHtfissmzriQ2FDbvr+L7NZnYTDrjf/ynNskxuH8lAg1b9In/V2sKK45diwYGDhtW+06z2zzH97L6jYnB69bZv+6kCPMGHU5LZgDTgIuFEPMBGxAqhHhTSnmDEOJmYCEwRzbm5AMQQpjxDf5vSSk/PnpfSlnYIM9/gC/b8RytoiojnSpPNKMHBs7CpTHy9vkmRwn9m45KltTnEfLyf8+evTdSWPgzNO07NO9YzjvvNcxmB/v2fc7hjBcxmTKpqd7RbJtudzXLl9+OxVJFkO16pPSF/zuc8QmpqTOaLOf11lNSspfi4r1UVh2kri4TrzcPRRRjthzfxMvPG37KbMTj8bBr94fk5b2L1boXRdExN9DDysvJmMoLCX7maRLOv7DZZ+gp7Fm2gr5Av1lN92tbyDq8kUFlO9kVdzYTkoa1qY4pc2fy+UcrAFhw87VYQtsWeWvwzBHsOFjF108t46qnrwe/OWpwbBh2bwVB1BE99vhhPxc+c+fo1OPtTbtqIIe2FLH560yGTzszlH93pVkFIKV8GN8GL0KIWcAD/sH/AnybvjOllHWNlRVCCOC/wD4p5TMnpSX49wgALgNOtS/rJHLWbQciSJrYtv8snUXe6jcRTCJ+6KQm84wa9QOio4exbdvtVFQ+jqpCSvJtmM2+tfphwy5m2LCL+errqdhs20lPX8aAAec2WldJySF27LwB1VSM1xvLuHE/RlXNfLd0PWbzF2Rn/4Agu53iot1UVB6kri4DjycXKMZsrj6hLl23oeuRIAYDfQlxDKC45EMEJzrZW7X6b1RWvkZQUCVmsxVFOZdhQ28nOnoYW7b8A21VNlF7llA1bx4jLjxzBn+AuvXrqXSEMGnMyA6rU+o6cW9dhEV6SbjyH22uZ9DVv+BKuwOb1Uzc3BvbXE/U8OHAeorqk9BrylBCfTMBIQSpCR72FsVTmZFPWL8E9r67Cl31K4D5c47V4Qi3EdXHQWluDaW5NUT16T1hQ7ua9hwEewGwAkt84zzrpZR3CiESgZellPPxzR5uBHYJIbb7yx0193xCCDEWX4iITOCOdsjSKnIPVBKkqkSOnNVVTbaIPGKJoRRLyOkPdiUkjCY8fBHLV9yBrpcyaNCpHjRttknAVxw69HqjCsDpLGbb9gtRFN+G39lTPzjmdG7smD+we8+1HDh4/Qll3G4bXk84QgxAEUk4HP2JiBhCbOxIIiP74P93cIzFS7YDK9F1nYqKTDZtvg6TqRiLxUpk5AOMHHEzZvNxq5JRCVdx+M1LqIyLY/xf/9KCHutZyIhIHLU17Nu2k5ETOyb2xJptXzNd8wVhCTuNhU5LSFl4V7vlUe12RvTLIz3LgeKIOiFt2IJR7H21gA1//xZ7TBg7skII8xSx8JdnYwk7cZCfcml/vvrHTlZ/cJBLftZ1cTp6G61SAFLK5cBy/3Wj5jNSyjxgvv96NdCoHZ+Usu2vGe1A6jo5xREkRXdd+MeWIHWdPOIZHNkys8egoHAuvKDpffc55/6dbxatQNePkJW1lqSkyajq8T+3oqgoik5t7SwWzP/XCc7WEhPHoSgfsG//uygi4tggHx3dt9kDaifK2Be320tlZTYbN92J2ew78tE36Q8MG3aK0Rj7HvgFVl0n8cknsJyBnkFn/vxe0r5bTOXvfs+wzz5CVU89rdsadF2H/b7ltcUj7+b8Zl4cuoraGp1gcw2c9P8rfspw+n28m4NVyXAEEpVs5j99OdaIU21LUkdFYw02kZtWjtvtxWLp1U4LOo1e16vl+/ZSp4XRZ7A30KKcQGX2buoIIjGx40zfpEzBZtvDofQb2bffiq73x+EYT58+s0lJnoauC0pLSxr1tBkfP4r4+FHtaj80pD8lpVBQsJ2jjsRU5ZJGB38AeeQIdUOHMnpy9zmZ3ZFERkdRdc//kfLXP3JgxEjSBw7BcdfdUFuDqmkIr5fq6hrm3n5Lk4q2rDSbgzu+QMlcSb+CDUx3lwEQFNt9Io3V1SnYba5G0+b+7hK++uUHRMZYmP6rG1BOowSHnBXPzmU5HFhfyMgZfTpL3F5Nr1MAORv3ADEknTUm0KKcQN6+TQAkDhrdYXVeMO9TCgt3k5X1HRUVGxGk4fHsIzPzLQ4dMmMySUJC2mZ33hIiIgZRUgp5+fcf2+T1eCqazG/2eHBVVeGpqsYc2n1OZ3ckc2+8hm+qKon9338ZcCgNfn7vCelRwOZBA5gy98Rlu3pXHVIx4fn3bM5yFVNkiSI9fgoZ/WYwdMIVnBPa9fEsmqLOFUREaOMKwGy3cukLN7SonqFTE9i5LIcje0oNBdBJ9DoFkJteS4hZIWxg4xujgSLvSAYKgrg22F43haIoJCSMJiHhuFIpLj5ARsZiyso34HLtIy5uQoe1dzJ9+kzgwMELECg4QgYRHjaAgQObdlFQO3QoUdu2sWfGDMSVVzL8/vswn2FLQYqisOCnd8FP7+KrhZeScCQT99PPEBQfR2F2Hn3v/z/KDqWDXwGUluWS8ekDjMj5Drvuxg6sjT2HqXd+Tmw3WsI8itQ06rwhBIdWN5+5GWL6hiAUKD7S/roMGqdXKQDd6yW3LIb+iUWBFuUU8kpriDWbMVtbFwiktcTEDCYmZjBwT6e2A6CqJs6b03LLlGlvv0XaF19Q98I/iHjzTXZ/8gnKNT9gxD0/xRTU/KGmnsb8Lz45YeNcGz6UnRYr4quv+Lymlkm3XEvlG1cwovowu/pdhAiOQVOtxIxY0K32rxriKs5Hx4Q9vGMUd3C4jdpyp3EorJPoVQqgZPs2XHowSac5aBUIpNdNvsvGsNjucygtEAghGHrxxciLLiLto49w/eNFwv77Cts/+piwB35O1a5deA6lo2RnYysvR9xxB6Pu+UmgxW4zJ1tNqapK5tTpDF2xFA6lkZ33LhPD9rN34X+ZPLHj3HJ3JpWHMwCwR50+nkVLSegfysHNTrJ2ldJvTMcdBDXw0atUas6WAwD0mTo+wJKAq7KEfUteJ2/XSor2rKIeG4l9ukdcgkAjhGDolVcyeel3VM2eTXBFBd5f/Rr7e+9j3bsX3W7H7PFQn54eaFE7nMteeoGQv/lOcA+vPcyec37PyB4w+Htqqtj0wut8+kY1Km5ihrcuollTTJifCsCmrzI6pD6DE+lVM4DcDDcR1kKC+wR+/X/H4jf4ek8lcNxneuKQwCum7oSiKAx79FEO9++PY8AAYidPIjQpCYB9Q4c1YWDc84mUHqoBee4jjJpzb7P5A4n0aqR99BnrV0lqvUkMiMlkyk3TCB84oPnCLSAq0UFwuIXiIzW467xY7L1qyOp0ek1vak4neZVxDEvtLv5FfKPX+f0VvJpEQyF+kHHg5WRCk/ow9hcPnHDvqNeRk5dQzhg8voNdirl7h/rMXbGcNZ/mUFyfSKw9l/OviSNx+q0d3s7oc/uy7uN0lry6lwV3d5yVnEEvUgCFmzfjlTaSRiQEWhQAIuMSYU8FMUkDGHTu9c0XMDiOXwHIM3AKoJXkU/LC8wCYkrqXq/KjVOzfy9o31pNRmorDZOO886oZfOl1CFP7DrY1xdjz+rJt8REyd5YYriE6mF6zB5Cz7TACjcSpEwMtCgB9J/jcN2zY1KiTVYPT0bjfwR6PdNaRfdU8ao/Uk3D3JVjPOj/QIp2As7iIVU/8j3eeyyGnLI4pY/K4/vELGHLlJZ02+INvKXDOzT6/XZ//bbvvBLRBh9BrZgA5RyQx9gJsUd3DkmD1uz7feFEhpw/+bnAq8ugAcIYtAbm3LKE+30PsFZMJ/7/HAi3OMbT6ena++QlbtoXg1pMYnpzN5FsuwJ7QdZ46U0dFkzIqiqxdpXz/5n7m3NS0x1yDltMrZgCe6ioKqxNISuo+7p8LSypwUMuFd515Ts86m6MK4EzbA1CCfaefiz7aFGBJfOiaxuJnFvHaA9+ydks8cWEl/ODuWGY9cmuXDv5HmX/HKCxBKvvXFpB38JTYUQZtoFcogPz1G9Ex0Wd09zGztNiCMAn9jF3O6BLOrPEf89jAW6cd5eCON/jm0Z9z8ICFei2Ui67QueivtxA1OnAuVBSTwoKf+Nr/5qUu8x5/RtMrloAK0vKAJOInd4/1f4DEPknsLi+m9OBGooZMCbQ4PYtjSvMM0wCAYoWwsfEBaz83czmlX/4fo0uyqFMnkcnFjP5hCMlTmo5R0ZUkDgwnNjWEosxq6mvcBDkCZylVU+fho0/SyN1VilKroWoSKcBjU7HEWJk4vQ8zz07q1ieYu69kHYgj0ncsvS4vL8CSHGfEVN8G3561iwIsSc/j2JzpDFsCAnwPp3T9c5WVpLH5lVnEvXoJA0qPsGHYXPQb/grAkbzu5TolZYQvzsD+dfnN5Ow88gtr+fvDq6hZVYRao6GHmdATbOixNpASc2Yde988xFM/Xc5Tj69n+Zrsbrl53StmALHDB8DKcop2HyB8aPeIAhbWZxBJ1jq2H/EyQ/OC2iv+FB3CmboJDICkS/381NWWsPvLuxix/zvGSp1tfcfS/5KXOCt6KF6vl5XKd5TkVjVfURcyfHoim77KJGNHCePmpgREhlf/sQ2HS5K4MJlL5g845S2/vNLJV18fpnJnMcEZtezJOMiaT9KZd+NQJo4J3AzvZHrFqBMxfDgq31OUUUb38ZoOUQ6VnFI75Vm7ieg/NtDi9ByOLgGdgQpASk4JpNIZeL0uti35Balb3may18OO6BSiFvyNSf1mH8tjMpmod1RCcffap3JE2DDbVAozqtC9Ooqp6xcy1FIXtWbB9JOWeL5cfJgd32ZhcklUXSIdJhY+PIFl32Vi21LKun/uYeWALO68Zzz2oMD7/uoVCkC1WIkOLuJQVii1v3kFKSWaLkCXKIqOoggUk0BRFVRVoJgUzGYFk0XBZFUxW1RMVjMmmxmzzYLJZsFss2KyB2GyB2G2OzAF2zHZHYhWRHkad9ZMdny9noMbFzPZUAAt5ky1AgL8M4DOey6p6+xc9xThq55lkrOOg44Iyuf+gTFjbmo0vxrlhbzgTpOnrQyeHM+elbls+jqTsy7u3+Xth46OxLOtjJd/u47bfjuF2Gg7S5ZnkfVxJuFAlRkcuiCoSmPRX7fiOCeW6383hdde2o4jvZbnHlnNVfeOY1BqeJfL3pBeoQAAho6xsm2TRkmFHSEkitARAqRU0KSCrgt0qaDpKrpU8UoLGk1tMHn9n9pTUkzCiUl4MCtuTKoXs+rFpOqYTBpmVWIyg8kMZouCy1VLUE0SO/YrlLy1DpNJQTUpqGYVk0nBZFZRTYpfMSkIVaCqKqrqy6cofqVlUlGOppkUwqNCsNqOy67rOnU1TrxeDc2joXl1dF0nNCIYu6Nz3U93BmfyEpCUstNmAAf2vIf320cYU1VCnsXGznN/wajpj5x2ySk03oY3I5TiyjJiwjouWl17OfvKAexdlcvO77MDogDuvGMc336fyf730vnP4xt5+PEZHHjX75xwVBg/vs53TuF//9qOu9gJq4r4yqPz4K+n8eFnabgX5fDxU1u56ddnkRAXOAXbaxTAyJt+wMjGX3IaR9fQ3fV4a+vw1tXiqXfiravH43ThrXfidbrxON14nR48Lg9etxePS8fr1vC6JV6PxOMFj0fB6xU4XRaqNRWvZsarm/BIK15pxQG4ayCjoL7DnlU4nNz91Pxjv994bAk1RxqfbkrFi7B4MdnBGqxitZuwBZsIcliYfdkEzFYTB3ceIWNfAcVHqqiv8oXSFIo4Nv4Kxf82Lo5fC9Egj/Df8/8+du3Pqygn5heKgnK0HlUcz6MITGYFNBdBEUPQCjxkL1qPNSocc4gdk92GyW7D7LCiWMzd2vqiSSQdrthyslZR/OVPGVecQYVqYuuEaxl1wTMkmpv32d+nbxRZ63T2HjrEzAndJ1SnxWIiZVQ0mTtLSN9WxIBxXR8Rbd7sVDIPVWDaUsbi77OOJ+yq5OXfrOOh58/lFw9PpabOw99+u4bw9SUsTsniykuGsDI2mG2vpfHKE5u470/TArYc1GsUQKtRVBSbA4vNgSWqc5qQuo7X5aam1kN9rROvx4vXo+Fxa3g92rHfuqajaTq6LtE1HV2TDa51pA6apiN1yeFN5UjP8QGkorT62OAfM1IQFGxGUX2DaV21m9oKiasaPHXgqYQaTSLQgHoOrV6OEuRF1vqCsUghEEcPLkvp88Ujj/9GiuPXiAbfx57Yn8fvzI2j1yevMbfgwN6Y/wNg16d1QF1jnYuiexFSR0FDkToC30c5+hHy+LciUYXv5bvhR1WFf3YliB8aw4ibz2tetvYgRYdtApeWppH+2Z2MO7KVKCHYPPhchl/8L8Y74lpcx5D+/cginczMfGZ2XvC4NjFxfiqZO0vYuyYvIAoA4PrrR/Di9pVs/zSD8PERaFt9B9TM3uP/ph12M/f8Zir/eXA1m5dkcf7sFGZMTSIvtwbTd3m88OxmfvnI1IDI32IFIIRQgc1ArpRyoRDiSeAiwA2kA7dIKSsaKXcB8DdABV6WUj7mvx8JvAekApnA1VLKXnW8TygK5iAbEUE2IqI7Jgbuv3Z+ja4d/8f37dvHT5Vefc/sxoqcgK7rVFXU8tHfVuOulegaJI5XGXPOAJIHJWDqQJ8vuu5XarqO5tWRuk/RaZqO1OSxdM2r+b41Da9bw+304qmqxVrrwlNZg7uyFq/Tjdetobk1NI+O16P7l7okmiaROuga6BJ03ffRJP6lP4FXU9ClgjymIlTft1CRQkVTzKSvqmPEzU0/j5QS3eXGU1OHu7oeb60TT60TT60LT70Lb53n+KzR6cXr8uB1abjqPbjqvLidkvqRdyLqLYj73kSXin9ZUkFKgS79Mkmfgph9gcqAixecIkddbQm7vryTkWlLGa/r7EwaTcolLzExpvXuE/r37Ysm9lOa1/3CMsalhqKaBIWHA2el5LCbGX/1QPa8c4iqHWUIBey6IPn8pBPyhYVYUQeGEHKgmvSsCgakhHPNlUN56lAF9sxa1mzMZdrkro973JoZwL3APuBoqJ8lwMNSSq8Q4nHgYeDBhgX8SuMfwFwgB9gkhPhcSrkXeAhYKqV8TAjxkP/3CeUNWo/uAbWBeyGvSwNUolpo/aooCuGRIdz2+ws7Rb6T2/K97Ko0ud3STVj6yDvsL43hnaueh9BwNKmi4f8IE7owoStmdKW5qbzA97ANHljqmDQXZtyYQ1XsDjfWIA+KIo/PRBTf8pqigiIEuzJTKM3KoaHXfa/XyfZvHyB127uc5fWwMyqZyIV/Y3y/tp8wVlUFZ0gV3pLuZQl0lMjEYIqP1OB1ezFZArOgcd7MFDRNsmVJFhJByqQ4rrpsyCn5Zs9LZc2B3Xzw6h4e+u00AG65cwyvPLKW5e8eYOrEhC5ftmxRjwkhkoAFwJ+B+wGklIsbZFkPXNlI0cnAISnlYX897wKXAHv937P8+V4DlmMogHajewW6B/79yNe4awTC7dMGc642Yg20h5RJKeR8dAiP2YFZKFjMApOqoaoaJpMbk0mgmhVMR63HLCZMVhNmm8lvPWbGZLdiDrJgDrZistuwhNgwO+xYwx0orZhZSSnZddf3HF06k7rOzjWPE7n670x0HbXs+SOjx9zYIc+uRmmQZ0dK2e0srxIHRVB8pIbFr+xl/p2BixUw79xU5p2beto8Y0fEsgYwFziP3YsKDyJuehzVK4t498M0rru6a88ptVRlPgf8EmhqneJWfMs5J9MHyG7wOwc4y38dJ6XMB5BS5gshGl3EE0LcDtwOkJyc3EJxey+KRaLXWPDUubGGQUg0DJ3Yh5iEiECL1qMZeNnZDLzs7ECLcQoHdr4Ji3/NmJoycqxB7Dz3QUZNf6hDD5OFxdtwZYRSUFlEQnjL9w+6grMvG0DahgIytpfw4eObufLB7uPupTGqYi2EFrlPCHJ//dXDeXxjMbUr8nFdOghrF85kmm1JCLEQKJJSbhFCzGok/VF8NpFvNVa8kXutmktKKf8N/Btg4sSJ3XMe2o24/bF5AB26Vm/QPdGy1jD44/coU01snnQDY+c9TZLJ1uHt9OkbzeF1bvamHyJhQvdSAIpJ4ea/TOW9P2+mMKOK5W/vZ9Z1QwMtVpMcjWaXW1BL30Tf+7RqUhgxry85nx3hg48OcMO1XefquiWvCdOAi4UQmcC7wLlCiDcBhBA3AwuB66Vs1K1lDtDQBWcScNQhT6EQIsFfTwLQvRyO9FBMJtUY/HsJGcHhbBpzKbb79zNxwT8wdcLgDzBkQCoARzILO6X+9mKymLjq0YlYglT2rMzj8PbuO5T0He47S1Fb5znh/kXz+lNlgex1BWjervMZ1KwCkFI+LKVMklKmAtcAy6SUN/itex4ELpZSNmKHB8AmYJAQop8QwuIv/7k/7XPgqE3FzcBn7XgOA4NeR3nfCUy67DXswZ0b5CilTwKa0CjphpZAR7FYTFz+gM9Oddnr+wMsTdM05f1dURRSpicQ6oYvFh3uMnnas1D4Ar49gSVCiO1CiH8BCCEShRBfA0gpvcA9wLf4LIjel1Lu8Zd/DJgrhDiIz0qo+4RAMjAwOIZqUnFGlmPaH82i1asCLU6TRPVx0G9MNK46L3tW5wZanEbJ3lkCgNl86tB75aWDqFFhz7LsU9I6i1YpACnlcinlQv/1QCllXynlWP/nTv/9PCnl/AZlvpZSDpZSDpBS/rnB/VIp5Rwp5SD/d1lHPZSBgUHHcuntk3AH1XHwTSdvfPBloMVpkpnX+cwvN32ZEWBJGkcI8CDp1zf0lDSLxUTkhCjC6yTLVh3pEnl64Fl5AwODrmZwSj/u+M08auILqVpq58WXPqTxbb/AEhxmJa5fKLUVbnLSut+5UqlDnU00ae9/zdXDcArJhiVZjaZ3NIYCMDAwaBHhoaHc/6urqR2Yg9wWybufds9gRjOv9Tl9X/3+wQBLciqWCAvBTtnkRm+Iw4I3wYa12I3L7e10eQwFYGBg0GKsJgv33XsdFTE5FC9WKSwtDbRIpxCTHEpotI3S3Boqi5uyTwkMoVFBmBBs31PMEz9ZyvN3LuWvv119Qp7B42OxSsGylZ2/F2AoAAODHkagl17MZhPJYyNQpYnFG1cGVJammHrZQABWvnsgwJKcSHCIzwXIvr2lBGsCBUFooZvKatexPOfNSsGDZPfGgk6Xx1AABgY9FNHoOcuuYfSoQQAUfx34qFaNMXBCLIoqKMmuCbQoJ+DwB7EvXZGPW0hME3znAl5/ddexPCEOC3WRZmRuXafHETYUgIFBD6WmxM3n3y3jX69+yIv/fb9Lg46PGTwUp6UGq8dOdX33GmSPYgs24zrpwFWgCQ3zKQATAneCjR/fNppqM9TtP9GjaZ8RkQRrgu17SjpVHkMBGBj0UIIzE8n+ELT1kchN0RSUde5gcTKDL/aZMm7csauZnIHBZFHoQp3YIqKijkfgswT7ghbpCjg0ePaZjeTk+w7bTZmSCMCWTfmdKo+hAAwMehiKohB3iZfw8+sYc3sY7hEFSHRiwrs2ZOPkCSOR6Bzam9d85gCgqErTR28DhNYgVkdCqk+BnnWFb7/CcqCGj9/3nWIeMiCSGhMUH6rsVHmMiGAGBj2QKy88/9j1/n1ZuFDIKy4kJaHrgorERURTE1yGktP55optQTUp3W38xxF8fM/kyH7f2dc5M5LZsTEf66Faps487jpNxlixFjjRvDqqqXPe1Y0ZgIFBD2fosBQAtuzc2+VtixgXSknggpqfDtENR7fB/SMImxVPjQpRicf7zePUqFckk8bGH7uXOCgMmxTsSuu8pT1jBmBg0MOJCAsFKqmurO/Sdl0eN16XJNgdTHVdDSF2R5e231O54ZrhcM2JLp/NVhX1pP2KUaNiWbuyiJ07ihk7onNiHndDHWlgYNAaNm72+VecOKHroknVuep55vF3Cc9PwjM+v1sO/q46b7ecBTSGzWHGhDjhPMDoYdG4haQgo/P2AXpI9xgYGDRFaXYtXsVNap+k5jN3APVuJ888+S6hOUnYz6nmvts7JvRkR1Nf7cYa1DMWOWx2n5y79h5f7lFNCnUOFW+xs6li7cZQAAYGPZyY5BBMuoXFK9d0elsur5tnn36XiJwUQmc4ueX6Szq9zbag6zpet05weOcEyeloYuJ9+wGVla4T7tsT7Dickppad6e0aygAA4MejKZpFK3VqA0qZ9qkcZ3alq7p/O3Z9wnLSsY2tZobr5vffKEAken3ux+T3P2WphojOsoOgNN5okVVUv8wVAQ793bORrChAAwMejBe3YvZY4MYJ7ERUZ3a1n/f+ozg9EQYX8ptN3fPN3/w+Upa/eEhAIZNSwywNC1D03w7wJr3RLvV+HifAisoqO2Udg0FYGDQg7GarXhSSjHnRuDyuJov0A4sEb5vT5XsUrcTreWl5zZTXeIkpI+dxIHhgRanWdxuLys+8LmuHj48+oS0wQPC0ZBk7O0cr6uGAjAw6OEER1mwaDZq6jrX9fFNCy/FNS4X86FoXn+ve0YF+/yrQ3jTqvAgyS2uo7Sia01j28KRvBrCa3wKdeTQE2dxcTHB1FgFrvLOUe6GAjAw6OGER/k2EF979ZtObUcIwQVzpwLgrPcNSFJK8otK+H7dRl5/90uee/Y9nvjNe3yzvOtjB6/fWkD6l1lU2RT6X5qCwy156bGNVNd0zgZqR/HB/3YDoA0/NUwkgLQoSHfnzLh6ho2UgYFBk1x36QKe/P4TbPtiyS8pJiE6ptPaWvLVFkzEUJXt4bFH3sVaFYLFe9TBmR3dBGapsH1RNhfMlAjR+S6rt+wp4psPDhBS4MKrwA/uG8eAlHBeKaxDWVfCS79YhUy28/MHz2oyFGMgWLMxl+UfHiK8SqPSJnjw7vGN5hMWBVHdOe42DAVgYHAGMGC+g9xPJe/8bj2x5wquveRCVFXt8HbqizVCAFNxKK7QKrz9y3DEBdEnOZpB/VPoF9+XN979mrpV8Wzct52zhneeZdKO3cV8/d5+HMVuQgDZL5irrxtOij/g+q03j+abxAwOf5QBWfX87/Xd3PbD0Z0mT0vYf6iMb79MpzarhrB6iU2RqOMi+dlNI5v096NaVZSKznFr3WIFIIRQgc1ArpRyoRDiKuB3wDBgspRycyNlhgDvNbjVH/iNlPI5IcTvgB8Dxf60R6SUX7fpKQwMejmXz5vL1uQ9LH5jJ5WL43hy8/vMvXE0E4aN6NB27nnoEipqqkiMjmvybXrhvOm8u2oLy5duZ3jqIIJt9g59866odvGfJzcRVOTCJkAbFMI1148gPv5Un0QXzu3HUpuJnW8fQF9fzGdxh7jkwoEdJsvp0Lw6mq5T59T4+JM08neUEl4nMQEWRaIND+OHN40gKjzotPWYGnET0VG0ZgZwL7APOLpQtRu4HHipqQJSyjRgLBxTILnAJw2yPCulfKoVMhgYGDTB+GEjGPPHobz7+Te4ljpY+7c8Vo/aya23XNRhrhoc9mAc9tM7f4uJjsTbpxL7nr68fv9GzGeXc/tNV3RI+wDfr87GXuSmVoXrfnMWSXGnf7Y55/Rl7Kho/vPrdeR8doQ/L8lmwNkJXHHpYMwd6GWz3umlrMLJkiUZ5G0tIaz+RJNOsyqRI8OZNrMv40a13LePOciEKgUer96h8kILFYAQIglYAPwZuB9ASrnPn9bStuYA6VLKrNaLaWBg0BJUVeX6yxZSOKOEN/73LUG7EvjH45/xi99c0ylLQk0x98pxfPv1BrRsG/r+jl2+mH9eP/7+RRYes2h28D9KVHgQt/zmLD58fz/huyop/S6Pf3+Xh2ewgxlzUxk7IrrNs5Tcgho+fG8f3v1V2KRvPAwDKsJU7HFBSE2iWhTu+8mENg3g1iATXqC8wklstL1NMjZFS2cAzwG/BELa0dY1wDsn3btHCHETvqWln0spy08uJIS4HbgdIDk5uR3NGxj0HuKionngget59b3P4PsE3v3sG66/fGGXtT9y2ABGDhvAc/94G2V3NDV1dTjsHTN4FRbVYdcFwi3xenVMLRxU42KC+clPJlDv9PLlN+nkLM7BcqCGdQd2s1SRuENNBEXbCIsOIjTMij3YTEiIhdBQC0E2E8l9QgmynThkvvnOXkpW5mOWgpowE5akYMaflcDUiQkdtuxls5uoAcorA6AAhBALgSIp5RYhxKy2NCKEsAAXAw83uP1P4I+A9H8/Ddx6clkp5b+BfwNMnDixm4V3MDDo3tx45UKe3P4+zu8dFM0qJTayc08Ln8yAEQkc2SVZv3UH502f2iF1vvH6LsKBqTcPa/Hg35Agm4mrLhsClw0hLb2M9evyqDpQjlLhwXSoBuehWhpzv1anSFLnJvnKAs8+vRHLwRpqHSoLbhnB2BGdY31lDzZTA1RWdrw5a0tmANOAi4UQ8wEbECqEeFNKeUMr2rkQ2CqlLDx6o+G1EOI/QPc8WWJg0INRVZXzbhjFuucLeOPVRfz8/uu7tP2zJ4wl473NpO3J4bzp7a+vptZDcFY9lSEqZ09OaHd9QwZEMmTA8VCaHq9OTl41peX1VFe7qa72UFvjxuX0Urm1hKJvc3khtwZ3vRdrei21quSXj83o8LX5hpSX+A6zVVV3/GGwZhWAlPJh/G/u/hnAA60c/AGu5aTlHyFEgpTyaMTjy/BtKhsYGHQwE0eMZNXwnVj3xrF2+zbOHtu5TuMaEhUSQU1YMabMjrE4/+q1PZgRmGNsnXLGwGxS6JccRr/ksFPSXD/w8swDKwndXYnVf+/aRyd36uAPUJpdQygQE9Oxyz/QjpPAQojLhBA5wFTgKyHEt/77iUKIrxvkswNzgY9PquIJIcQuIcROYDZwX1tlMTAwOD033XwhTkstK99Jw+Pt2hi+Qck6QeUR1Na2zy3DgY0FlOz0xdG1H67j7ff3dYR4LcZqMXHR3WOoCBJUx1n40d9n0DexPduiLUNRBXWqZMzwjl9iapUCkFIul1Iu9F9/IqVMklJapZRxUsp5/vt5Usr5DcrUSSmjpJSVJ9V1o5RylJRytJTy4gazAQMDgw4mKiyCvudbCamM5e2Pu/a4zYDhCSiobNi2q811VBbX8d2r+0BAfZQvsHr2yq4fMkYOjeLRZ2fz0O+nY7V0zTlaqfs2SjuD7nMu2sDAoFO5av75VEcXULpSIa+kqMvanTp+LJrwkrY7u03lK4vrePPX65G6BAlBpR5qVcmgeX07WNLuiVbvxWvunKHaUAAGBr0ERVG48MZxmDUrb/1vSZe1GxMaRVV4ITVZbXuP3fB5xin3Hnh+NpdfNKi9ovUIhFODIEMBGBgYtJMxQ4ahjyrBnp7AY795m7c+7hrju6BkHVt5+Gn3AdxOL0ca8Xt/ZE8piiqosB/f9F2y4kinyNnV1NV72LariH++tJXnn99MQdGJgV90XcfqkajB5k5p33AGZ2DQy7j1hxfxyqtfELIznorF8PiaDzEnuxkwIp5550zDZrU2X0kr6T88gbwdChu37mb2OZMazfP9G/s5tKWI8Dg7l/18PPZQC6V5NbjqvMQPCGXmJf34/pkdAOxcn8+saUldtg7fEew7WMbypZl4XBrVeXWgS8Krjzv5UYD//WkDNzw06djm8rZdxdh0gSXx9O432ooxAzAw6GWE2B3ce/e1XP6nUVimlyMdLpS0SHI+hFff+bxT2jx73Fi8wkPa7pwm85T7wx5WFNbx2sNrSFufz5avMwEYfa5vvd/j3w61Z9Xz4r0rum1ksooqF+WVTpYsz+Iv9y7j+TuXsuzp7ejbK1D3VRNeqWGp06hOsFKfamf2/WMY+IP+BLkl7/5pI4uW+pa9ln6RjkdIFi7oHAd2PUd9GhgYdCgJ0TH8+Aafk7aaulpeu38Durdz7E3iQmOpjCjAntW07x5XnRdFFUy5tD/rPk4/ZvWjmgQDxsWgKApLR4TBnioALLLzYw20FI9X529PbcRV4sTk1LF7JQrH/QKBQBkXzrCRMUwcF8+K1dmcNyv5hBnM8MFR2Gwmdr92gK1fZjJ3Zgoir566KAsJcZ0zAzAUgIGBAZr/TVq1dN6iQFAfsO4Kxe3xYDEfX9P2enVWv3eAmnIXQSFmxs1Nof/YGD55eiu1FW6ShkaiKAr5hbWIPZWAoCrGzI13j+2UAC+6rrNhayFer87EsXHH/P8Ul9UT5jBjaTBoe7w6r7+xi+KtpYR4IAioirWgR1gRZoX4vg5mz0wm3GE9wd//gvP7N9r2+FGx7CANc2wQazblYdcFEaOjG83bERgKwMDAgDqnz/uN2dxxHkN1XeeVdz4lKiaUS847l4T4KCp3qWTkZzMkuT/uOi8r3zvAwc2F6JpENSvMvmEoAGExdm76y9kc2lxE//E+18maruMR4BWSnz50Fo5gS4fJepQjuVW8+eQWwpy+mdBqSxoj5qdw+LNMzFLgFJKJNw3hnLMS+eizg2QuycGuCzBD9PmJXLZw4AkKorUsW3EEFcHISfHs2ekz1Z1xTlKHPFtjGArAwMCA9Vt3AAphER0TN+Ddz78hc1M5IcXx5AFP73gHZx5EYufw7kKGJPfnfw+txuvWMZkVRs9OYuqlA1AavCUrisLgyfHHfuu6xCIFFglffnOYa64c2iGyNuT9N/bicOrYz47FYlXxfJ9P9qdZmP3LOTYpWPN1Bmu+OExYmRevReA4K5a7rh3eIbORtG1FmIVk1vS+PLcyF1WVJCV03mljQwEYGBiQub8IoUSycM7sdtVTVF7KJ59/j3ddJCHEU9c/D61CxZTtINLtG8gKVnhJjy9C8/qWnX78txktGjx37y0BoDLSxPkT45vJ3To8Xp0PPk5DzaqlJtrC/900CoBVKWEc2F9KWX4tSLAcqUNUegh1g3uwg5/fM75db/wN0XUd8uupjzRjMSmopS68YZ1j/nkUQwEYGBiguSWq0Npdz1tvfItl7/HB+bJrzmHF6q04V4ZQ4yjFURNFXaWHRf/2+X7s79/cbQlW/+xg2MQ4BqWGt0vORUsz2LmhAE+VG+HUsLskZimQAi7/4fEwmudM6cM5U/oc+/2Xe5cR5gIvkiuvGdZhgz/Axu2FBGuCkBGRLFudTbAmiBzXOS6mj2KYgRoYGDBkfB8sWhBffLeiXfUI5UTLnO270wgOsQEQM8FEnbXqWFrcBAsX3jGqxXX37x8OQPaesnbJmJVTRfoHGQQfqUep05AWFWecDWVcOLc/dQ5DB0Y2WXbKlQOp72dnzI1DOtwR3KY1uQDMnpXC1tW5eIRk/gWNbxZ3FMYMwMDAgPkzz+GZzz8nfXOdL3pHG6ipqyUoxExDX6P5R8qYO28S332VQf2KMOxAeVABEfXxLMtZxpW0PEhAv5RwKh0KSnH7vIp+9lEaKjDwB/2ZNzu1VWXPPSeZc8/pnMiEVYerEUGC6MggTLn11MdYCQvp+EN5DTFmAAYGBqiqihZWh6xtmxXQrkNp/O/+dXjXRVJnrSL8gnpqk/OZes5IhvYbQE3McedzEfW+JaJxA0e2up3QIDMhbliztW2eQA8cLkfuq6IiVGHuzO4TYjYnv5qwep2Q/iEs+i4DmxSMOLv9AW+aw1AABgYGAKjBEtXZtjdOk6Ki+IcT04A6rr90Ab985HomjvAN8mdfMojq2AKG3xqMc0gBNX3yue6q1k813NJnnvnZslMdxDWH5tX54J870AVcdeeYTjlD0Fa+X34EgWDi1D7s21BAvSKZd25Kp7drLAEZGBgAYHGoCFcwuq63enAc1n8gn4VsJaQ6mnPnnRpxbMbEScyY6PMBNHvyWW2Sb09aKeYSF/lm+OP/TW51+f+9sYvwah37tFgG949okwydRfaeUsyqZPDAcDYUu3En27vEz1H3UYEGBgYBJTjUiipNlFSWt6n8gjvGALBl6/6OFOsYGzfkoSBYeMvw0w6Ou/YVk55VAUBJeT3L12Tzt2c3UbuhhIowlZuvH9Fk2UBQ7/QSVOpBj7fxzaIMzAgmz+y8w18NMWYABgYGAISG2ykGCkqKiY2IanV5uzUIgLpqdwdL5sPr8ZmpOuy+E8DllU4Oplew6rtMgsOslB6uwlzjJVjzWSJ5kMcOcKlIqqLM3HbvhG619AOwcm0OFilIHhPD/jX5mEwwY2qf5gt2AIYCMDAwACAyMpRinBSXlEMbYq2s27QTsMPWKDxeL2ZTxw0vazbmUrO5FJdJkJtfzWf/2014pU8h2ACNOkwm0KKsyNgg6qvduCvchCc76JMSxuDBEQwf3Hql1hU4HGYkkgPf5RDqlngHh3SZkjIUgIGBAQAxURGkkU9ZWVXzmf3szzzMZ2+txexQsO7zWffUBpejS432DC/Fu4opfv8AUhF86/FiLfPiVSDx7Dj2v5eORYHqeBtJQyMYODiC+Jhg+iQ4MJu619t9S5g2uQ8Z6ZV4VuZTG2/jth+2/GxEezEUgIGBAQAJMbFAPpUVtc3mPcqnL20kpDzx2O+hNwcxZ+pVbZbBW+/lwCu7cRypwgaowPka7DUJcqIs5K4vRJgFt/52CrHR9ja309244drh6D8Y2uXLUy1uTQihCiG2CSG+9P++SgixRwihCyEmnqZcphBilxBiuxBic4P7kUKIJUKIg/7v7rUtb2DQy4gOj0Ci46z1nDZfblEBXyxbzqKVq3CUR1OdeNwmf8akxqN9tYTaglrS/7CO0OxqqkOtxN03gerkUMJNgrMdJs6p0Qh1Q+rMxDNq8D9KIPYmWjMDuBfYB4T6f+8GLgdeakHZ2VLKkpPuPQQslVI+JoR4yP/7wVbIY2Bg0IEoioJHdeOt8PDiy+/j2eoLZeKy1aAkOrn82hmkpWdy4N06TLoZ0BEohCSYqDHlIQRtXvd3lzvJ+z6bYAmuSXGMuGIwAENvHcnBV/cQklVFgllB6WfliksHd9Qj93pa9NcSQiQBC4A/A/cDSCn3+dPa2vYlwCz/9WvAcgwFYGAQUMyaBcvhRORhEMKLd2gJslpiTo/mmz8eBECqOv2u9Q0dWekFXHjBNFIT22a2qHt1Mp/biqWkniD/vcjRxx2gFW4qwJJZhReJflYCd11+4u50VVYmtogILKFhbWq/t9NSdf0c8EugLd6PJLBYCCGBl6SU//bfj5NS5gNIKfOFELGNFRZC3A7cDpCc3H2ObhsYnInIcSXU77My6opo+qf0Z3Dy+QAsW7+efa/WAXDFb8bSN87vpmBm29sqSyuj8I19hPjdQtclh6JE20gcEIbm1jjwyh6CMyrQhCD0+mHEjDrRM2bae2/zzYdvYpOCC267m9QLF7RdmF5KswpACLEQKJJSbhFCzGpDG9OklHn+AX6JEGK/lHJlSwv7Fca/ASZOnNg5AUsNDAwA+Okd1zR6f0BKX/aRBkBsZNPeMluC7tU5+MY+bPtLsQHOCXH0v3LQsTXwou3FlL2fRoguqXRY6H/PWIIibCfUsePfL7JsyVcEo+BF5+P/vUi/j99j2LnnM/TaG9olX2+iJTOAacDFQoj5+ExuQ4UQb0opW9TLUso8/3eREOITYDKwEigUQiT43/4TgKLT1WNgYBA44iKjqY5fjhoMVnPbPVQ2fOuvDDLT99aRhCYfX1jIXnoEuTgTMwL32YmMuGTACeXriovY8MyTbE3fS6SicvUzL4KqsvjXD5JVUcLhT9/lyOYNnPfk37rdga/uiJCy5S/V/hnAA1LKhQ3uLfff29xI/mBAkVJW+6+XAH+QUi4SQjwJlDbYBI6UUv7ydO1PnDhRbt58SjMGBgbdiOrcagBC+hwf2Bu+9euANj6O/lcNOmGQriupJ+/JzWgKJN03nuDY4BPqXfrg/exO349XVUg0B3H58y9hjTg+G3HX1vLVz+7icFUZF11xPYOvvrZzH7QHIYTYIqU8xVqzzSpSCHGZECIHmAp8JYT41n8/UQjxtT9bHLBaCLED2Ah8JaVc5E97DJgrhDgIzPX/NjAw6MFkLz1C2d+3UfT37ZQf9PkUKksrI+136whOK6MmyEzUT8Yx8AdDThj8KzMryXp2CxYk4VcOPmXwX/fXP7I98wCxQcFccevdXPvmBycM/gCW4GDm/fEJAA6981YnP+mZQatstqSUy/FZ6yCl/AT4pJE8ecB8//VhYEwTdZUCc1olrYGBQbemalsRYUIgpKTkv7sp6BeG/XCFb61/fBzDTnrr13Wd7CVH0JZlY0OiTU8ibkLcsfRNzzzBjvWrqBSSONXC1a+8hWq2NNm+PT4em9tLVU1dZz7mGYNxEtjAwKDjqPVQB4RcNgj9k4MEZVRSGWQm+baRhPT1LQm5q90UbS2iJq0cMitx6BKnIoi5eQSRQ4+/1R/5bjErN6wkVMKU0ROYeM99px38j2I2mSChY4PGn6kYCsDAwKBD2PSfXcTVeSi3mRg8JYF8wFlSz7D5qcfe+ot3FVP15n6sAhxAjSKoGx5Fv8sHYnGcOLjv+Og9FF1y3d/+Q3Bi4qkNNoFFUXF7Tj3N7K6u5sO7bsHlcZMQ34fzn34epQMd1vVEevfTGxgYdAi1BbVEHCynRsKgn/oCwiRMOR7SsCa/lrxlR1B3+hwCeKb1IfasOJJOWus/iruqkozifPqGhLdq8AcwqyaKPPUUbdlE7ITjrimW/fpB8j1OwqVgT0E2Ke++zbAbbmrto55RGArAwMCg3WS+vheHAMdVg6nNq6FgTR6uvBr0MidqrYdgTccuBLWKIPrGYUQNP71r5k1/fwaPqjDqgoWnzdcYw84+h6IVi3nz8d+R4ghn3KVXExQTzd68LPqHRrHgb//kXzdfxZ5l3xoKINACGBgYnAH4XcI4PzyASQgsgAWoBzxWlepEB9HT+5A4OqpZ+/y9b7zKhp2biVEtDLri6laLMv6enzHgwoWsefZJDhRmk/nWfwAw6ZI5v/4DluBgrEJB83pbXfeZhqEADAwM2k3s+SnUv72PmiAz1pFRhPQLI2JwBJaQ5jdtG5L17SIWf/4+oULlyr+/1OY1+rABA5n/wkvMLi5h1+v/5eDWjQydNo3QlFQAahQIarMbs45h0fpdDEvtQ0p8+05WtwdDARgYGLSbmDEx1CeHkBRmafMJXG99PYtffgGLhKsefw57TKPuwVpFUEw0k3/+II2FkC92tjzuQUeTW1zB+kUfsR644KobmTJiQLNlOgPjrLSBgUGHEBRha5f7hbV//SNVCpxzwcWE9e/8ATExNHAhSKLCjm9+L/rgDXKKygMih6EADAwMAk552n627ttOvMnKiFtv79S2dN3nfTQ6LHAKwGYxM23+8chpL7z44jG5uhJjCcjAwKBLcVdXk7noa+pKigmKjCQoKpp1r/8XieD8nz/S6U7cvDU+X0Umi62ZnJ3L3MkjGDs4mX889zQ2PLz/3QauOX9ql8pgKAADA4MuQfO4+eDWG8h31qIrp+7AjkkdRMz4CZ0uh6uyEgCzLbAKACAqNBgXJqx42bJhLVefd1aXejE1FICBgUGXULB+HbnuOvraQxg++zwi+g+kvqSE+vIyvC4no275cavq03Udb001UpcIVUUxmRCKglBVUJQmB1JPtX8GYGuZW2td1/HoEpOioDaiuNrDpv1ZWPFSaw4n2FPBx8u3cOW5bY+r3FoMBWBgYNAlVGVlAdBn8FBG/vBHLS5XmX6IzO8Wk7NrG4XFhTg1L17AqwhkcyFppUSVEkX6BjsV4Qtjq4AlqPFTyA1ZvGE3y7/5BCEl5TKIu354PaP6t+5k8ulYuWkHALdc/wNee/VVNqxdzeWzJnTZLMBQAAYGBl3CoCuuIuarT9i4fRPxn3/CgIsvazRf+YE09n/0PnkH91NUVUGd6hvkFV0SqZqJC43EYrVisQVhCQpCKApS09F1DZBIKZG6BCmRSLxuD16PG6/Hg9frQdM0QoDUc5t3Rrz74GEsaBzRw4lXqnj9nff5y4M/xWxS290fZVW1lGbsRbNEMDw1geTh4yneu46v1u7koulj211/SzAUgIGBQZdgCgriiqee54177+Dr1//Ddf0GEDVq9Al59r35Oks+fRePqmDRdGLsDoanDiBlyjSSZs3GZG/+rb0jmTRqGEsObiXK4sUaPRBL4QFeeO8b7ru+9S4qGqLrOk/9912s0s30uZcCcPNFs/jT3i2sXLmyyxRAqyKCBRojIpiBQc+nYMN63n/yDwQJhRtefJWgmGgA1v7l96zbvpFQKZj/0wdImD6jW4R1fH/pBrau+g6L9HB0C8AaGsW9d9+B3da6k84AHq/GY//9AC1/P9a+I3n4tiuPpT371pdUHtzMtPlXMXfyiI56hI6PCGZgYGDQFmInTSYxJJwqBfa/74vcpXncbNq2gRjVwo3/eo0+M2Z1i8Ef4Oo5Z3Hf//0Ul/34yWRXVSm/e+I5iiuqW1yPruvsPpzHI0+8gJa/Hy2yHw/ecvkJeW655FxcmFiybHlHiX9ajCUgAwODLsNdVcnn995FVl0V/UMjGeU/9JWz/Hu8isKIqedgizq9p9BAEBcZyn0/up7n//533CY7Ccn9IWMH7y1ey8IZE3jxf++Auw5LeCyq6htWFVVFEQpujwtXTRVWZxmqkFilQuLYc/jRJbNPUXLhDjuR/UZSm7GdldsPMGPs4E59LkMBGBgYdAnlafv55Ne/pFzojO8/jJl/fvzYALj2tZdRdZ1Bl1zeTC2BIz4qjHEzL2Dniq8oyEzDDuTv2cC/jmRhd5X6MpVn4fYPq0LqKEi8wgSKGTV2AOEREZw7eQyjByY12c4tl57HM8/u4pulKwwFYGBg0PMp27+Xt3/1AF4B58+7lFG3HXf3sO/N18nz1DNhyKhj3jq7K1eeO4nK6hrSdmwCCWahY67JO5beb9Icbl5wTrvaiA5zYIntj154iKyCsk71FtriRTYhhCqE2CaE+NL/+yohxB4hhC6EOGVzwZ+nrxDieyHEPn/eexuk/U4IkSuE2O7/zG//4xgYGHRHXOXluFSFESmDThj8vXW1rPz0PYI1ydkP/yaAErac2y6ZzWO/eoARM04csuqFjfMmj+qQNsaOHIYiJHszcjqkvqZozS7LvcC+Br93A5cDK09Txgv8XEo5DJgC/EQIMbxB+rNSyrH+z9etkMXAwKAHkTB1GtFC5VDmITSP+9j9tY/9mRpVMH3hZViCu9bEsz0oisJV507mFw89DLGDADDpbt7+ejk19a52169pXeMYrkUKQAiRBCwAXj56T0q5T0qZdrpyUsp8KeVW/3U1PgXSp+3iGhgY9FTGzJxLnSpY//ifAag8nM62vduJV60Mv+nWAEvXNoJtVn539/VcedPteINjqM3Yzh8ff4Zn3vyc3OKKNtd74HAmAOOHpHaInE3R0j2A54BfAiFtbUgIkQqMAzY0uH2PEOImYDO+mUJgnGIbGBh0OqPvuJvdq75n445NDNq8kTUvvYCmCObc+4tuY/LZVkb2T+TxX97F56u2sWrVSqoObeU/h7ZSawql74ChnDd1HJEhdpZs2EV1bR3xMVEMTUlgUN+4Y6eKdV1n1Y6DfLd6PaIkk3prBHGRoZ0qd7MHwYQQC4H5Usq7hRCzgAeklAsbpC/332vyhJYQwgGsAP4spfzYfy8OKAEk8EcgQUp5ymuAEOJ24HaA5OTkCVl+fyIGBgY9j/K0/bzx6P1YEdQoMCyuL/Of/1egxepw1u5KZ9XmHZTkHCZYq2kynyYFLjUIYQtBd9YQrNfilQpE9uWnN17RYQqgqYNgLVEAfwVuxLeebwNCgY+llDf405dzGgUghDADXwLfSimfaSJPKvCllHLk6WQxTgIbGPR8tr7wHN+v+g6LpnPbP/6HPS4u0CJ1Klv2Z7J+ZxpOl4uBKX0Z1i+Rw7lFZOUWUlRcQk1VOdJZg1TNpA4axtXnn01MeJsXWxqlKQXQ7BKQlPJh4GF/JbPwDfY3tLBRAfwX2Hfy4C+ESJBS5vt/XoZvU9nAwOAMZ/w9P6O2pJi4kWPO+MEfYMLQVCYMTT3h3uC+cUDHWAy1hzafAxBCXAY8D8QAXwkhtksp5wkhEoGXpZTzgWn4Zg+7hBDb/UUf8Vv8PCGEGItvCSgTuKPNT2FgYNCjOOd3fw60CAYYzuAMDAwMzngMZ3AGBgYGBidgKAADAwODXoqhAAwMDAx6KYYCMDAwMOilGArAwMDAoJdiKAADAwODXoqhAAwMDAx6KT3qHIAQohhojzOgaHz+h7o7PUVO6Dmy9hQ5oefI2lPkhJ4ja2fJmSKljDn5Zo9SAO1FCLG5scMQ3Y2eIif0HFl7ipzQc2TtKXJCz5G1q+U0loAMDAwMeimGAjAwMDDopfQ2BfDvQAvQQnqKnNBzZO0pckLPkbWnyAk9R9YulbNX7QEYGBgYGBynt80ADAwMDAz8GArAwMDAoJdyxikAIcR7Qojt/k/m0UA0QojJDe7v8Ae0aaz874QQuQ3yzu+mckYKIZYIIQ76vyM6Q85mZJ0rhNgihNjl/z63ifKB7tOWytklfXoaOaOEEN8LIWqEEC+cpnyX9GcHyRrQPvWnPSyEOCSESBNCzGuifMD7tBWydlyfSinP2A/wNPAb/7UdMPmvE4Cio79PKvM7fGEvu7ucTwAP+a8fAh4PgKzjgET/9Uggt4kyge7TlsrZ5X16kpzBwHTgTuCF05Tp8v5sh6yB7tPhwA7ACvQD0gG1m/ZpS2XtsD4942YARxFCCOBq4B0AKWWdlNLrT7bhC0UZcNoh5yXAa/7r14BLO1FMoFFZt0kp8/zJewCbEMLa2XI0Rzvk7NI+bUTOWinlasDZme22hXbIGtA+9bf/rpTSJaXMAA4BkztThpbSDlk7rE/PWAUAnAMUSikPHr0hhDhLCLEH2AXc2WCgPZl7hBA7hRCvdObSSjvljJNS5gP4v2M7Wc5GZW3AFcA2KaWribIB7dMGnE7Oru7T08nZHF3Zn9B2WQPdp32A7AbpOf57jRHoPm2prB3Wpz1SAQghvhNC7G7kc0mDbNdyXLMCIKXcIKUcAUwCHhZC2Bqp/p/AAGAskI9vitYd5exQ2iqrv+wI4HHgjiaqD3iftlDODqM9craADuvPLpC1w2ijnKKRqhqbVXeHPm2prB1HV695ddG6mgkoBJJOk+d7YGIz9aQCu7ujnEAakOC/TgDSAtGnQBJwAJjWwnoC0qctkbMr+/R0f3vgh5xmXb0r+7O9sga6T4GHgYcb/P4WmNod+7SlsnZkn/bIGUALOA/YL6XMOXpDCNFPCGHyX6cAQ4DMkwsKIRIa/LwM2N0d5QQ+B272X98MfNaJckLjsoYDX+H7R7umqYLdoE/DaYGcdG2fniJnS+ni/oR2yErg+/Rz4BohhFUI0Q8YBGw8uWA36dMWyUpH9mlnarlAfYBX8a2dN7x3I74NwO3AVuDSBmkv43/LBt7At/a+09/RCd1UzihgKXDQ/x0ZgD79FVDrl/XoJ7Yb9mlL5eyyPm1MTv/9TKAMqMG3Bjw8kP3ZAbJ2hz59FJ9FTRpwYYP73bFPWyJrh/Wp4QrCwMDAoJdypi4BGRgYGBg0g6EADAwMDHophgIwMDAw6KUYCsDAwMCgl2IoAAMDA4NeiqEADAwMDHophgIwMDAw6KX8P4UFPxrLSBnnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "in the below map, we also show the non-included water tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CREATE COUNTY-LEVEL MAP OF THIS STATE - excluding lakeshore tracts from county building\n",
    "for s in skipList :\n",
    "    countyNo[s] = -999  #don't count these lakeshore tracts as part of a county\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1)])\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "countyTractList = [[]] * nCounties  #this should work but doesn't for me\n",
    "for c in range(nCounties):\n",
    "    countyTractList[c] = []\n",
    "waterGeom = []\n",
    "nCountyTracts = [0]*nCounties  #how many tracts found in this county\n",
    "for t in range(nTracts):  #now loop through tracts, assign each to county, build county polygons\n",
    "    if t%500 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isLakeTract[t] == 1:\n",
    "        if waterGeom == []:\n",
    "            waterGeom = tractGeom[t]\n",
    "        else:\n",
    "            waterGeom = waterGeom.union(tractGeom[t])\n",
    "    else:  #not a lakeshore tract; add it to map\n",
    "        c = int(countyNo[t])\n",
    "        nCountyTracts[c] +=1   #found another tract that belongs to this county\n",
    "        countyTractList[c].append(t)\n",
    "        if nCountyTracts[c] == 1:  #this is the first tract found for this county\n",
    "            countyGeom[c] = tractGeom[t]  #seed the county geometry polygon\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "countyMAP = []\n",
    "for c in range(nCounties):  #now build the state map from the counties\n",
    "    plotPoly(countyGeom[c])\n",
    "    if countyMAP == []:  #is this the first county we are adding to map?\n",
    "        countyMAP = countyGeom[c] \n",
    "    else:\n",
    "        countyMAP = countyMAP.union(countyGeom[c])\n",
    "plotPoly(countyMAP)\n",
    "plt.show()\n",
    "print(\"in the below map, we also show the non-included water tracts\")\n",
    "plotPoly(countyMAP)\n",
    "plotPoly(waterGeom)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2293a44e-2406-442d-8cdd-9c0c232898db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now clip off the SE coastal region and assign its pop and voters to nearby tracts / precincts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now clip off the SE coastal region and assign its pop and voters to nearby tracts / precincts\")\n",
    "pt1 = Point(-71.5,41.15)\n",
    "pt2 = Point(-69.8,41.15)\n",
    "pt3 = Point(-69.8,42.5)\n",
    "pt4 = Point(-71.5,41.2)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #all SE MA\n",
    "plotPoly(countyMAP)\n",
    "plotPoly(clipPoly)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8c21ae89-e8ad-45a1-8a44-e33018848c92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  97 tracts w pop 377609.0  to reassign to tracts...\n",
      "[27, 30, 31, 355, 357, 364, 367, 369, 372, 374, 823, 884, 888, 894, 1050, 1054, 1086, 1087, 1088, 1089, 1090, 1098, 1099, 1117, 1122, 1229, 1230, 1231, 1232, 1254, 1255, 1256, 1257]\n",
      "I have flagged  120 precincts to reassign to precincts...\n",
      "[139, 140, 142, 189, 190, 202, 784, 785, 866, 867, 868, 1156, 1157, 1158, 1159, 1344, 1345, 1424, 1426, 1439, 1440, 1441, 1508, 1509, 1510, 1524, 1566, 1567, 1573, 1619, 1620, 1621, 1622, 1625, 1782, 1783, 1784, 1785, 1786, 1826, 1901]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > ... if need to constrain more :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x > ... :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f890646-c926-45a5-a858-17d718cd0606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bba80c07-daa2-4fa4-b64f-050bc27a5b70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the tracts receiving clipped population\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and here are the PRECINCT receivers of clipped voters\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "print(\"Here are the tracts receiving clipped population\")\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()\n",
    "\n",
    "print(\"... and here are the PRECINCT receivers of clipped voters\")\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9012b9a-37c0-4f8e-821b-7c019e295bba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  377609.0 people (VAP of 317379.0 ) and  256955.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d9876e2-babc-41ce-8a76-476e73a6b5d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now build the state map from tracts, excluding the skipped ones\n",
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now build the state map from tracts, excluding the skipped ones\")\n",
    "countyPop = [0.]*nCounties  #these may differ from true if we clipped map corners\n",
    "for c in range(nCounties):\n",
    "    for t in countyTractList[c]:\n",
    "        countyPop[c] += tractPop[t]\n",
    "liveCountyList = []\n",
    "for c in range(nCounties):\n",
    "    if countyPop[c] > 0:\n",
    "        liveCountyList.append(c)\n",
    "nLiveCounties = len(liveCountyList)  #how many counties have some population after our clipping\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plotPoly(countyMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ec8af54-3613-43c9-9b5e-ded483c346e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.32783705140640157 1031\n",
      "100 0.2800632911392405 1264\n",
      "200 0.27476415094339623 848\n",
      "300 0.3952569169960474 506\n",
      "400 0.03594771241830065 306\n",
      "500 0.06773920406435224 1181\n",
      "600 0.0 0\n",
      "700 0.29935992122107336 2031\n",
      "800 0.32530737704918034 1952\n",
      "900 0.36553524804177545 383\n",
      "1000 0.45410917816436713 1667\n",
      "1100 0.27048458149779736 2270\n",
      "1200 0.14193962748876043 1557\n",
      "1300 0.0 0\n",
      "1400 0.28957528957528955 777\n",
      "1500 0.38507109004739337 1688\n",
      "1600 0.44268605645851156 2338\n",
      "1700 0.07764156450671336 1713\n",
      "1800 0.07852564102564102 624\n",
      "1900 0.48547535211267606 2272\n",
      "2000 0.4527056753189617 2273\n",
      "2100 0.49221183800623053 1605\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "plotPoly(countyMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "52bbc7ec-d300-42d0-9427-6c9d40fcac12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 2.395143641398515 2.138584441363504\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "print(\"original and final map areas are\",countyMAP.area,MAP.area)  #same if we didn't buffer\n",
    "plotPoly(countyMAP)\n",
    "plotPoly(tractMAP)\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb608f09-2e8a-424f-8bf9-7aebee031e24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MA has 9.0 congressional districts\n",
      "2020 population, Trump+Biden voters, lean =  7029894 3549355 0.3288445045985185\n",
      "compare to Census 7029917 Leip has Trump + Biden = 3549404.0 0.3288445045985185\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 7029917\n",
    "atlasTrump = 1167202.\n",
    "atlasBiden = 2382202.\n",
    "nDistricts = round(censusPop/770000,0)\n",
    "print(STATE,\"has\",nDistricts,\"congressional districts\")\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "447e2483-a294-4b44-9715-61d192c2d0f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this cell debugs the list of live counties\n",
      "[1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13]\n",
      "[0.0, 129026.0, 811259.0, 0.0, 809829.0, 71029.0, 465825.0, 162308.0, 1632002.0, 0.0, 725981.0, 562588.0, 797936.0, 862111.0]\n",
      "avg district pop is 781099.333\n"
     ]
    }
   ],
   "source": [
    "print(\"this cell debugs the list of live counties\")\n",
    "print(liveCountyList)\n",
    "print(countyPop)\n",
    "print(\"avg district pop is\",r3(np.sum(countyPop)/nDistricts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "42a126fc-b2df-4b2f-9038-809603def2c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time to ESTIMATE POP'N DENSITY on a county level to improve initial guesses for wedge radii\n",
      "map has a total of 11 live counties for 9 districts\n",
      "avgCountyDensity, maxCountyDensity are 298833.78 41198295.296\n",
      "here is a map of log county density.  All district diameters should be less than 3.285\n",
      "I will now assign precincts to counties\n",
      "uh-oh. precinct center 44 not found in any county !!\n",
      "but this precinct intersects county 13\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"time to ESTIMATE POP'N DENSITY on a county level to improve initial guesses for wedge radii\")\n",
    "# also, IDENTIFY WHICH VTD's INTERSECT EACH GRID to speed later intersection searches by grid\n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 9   #MA CONGRESSIONAL\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "MAPdiagonal = ((MAPMaxY - MAPMinY)**2 + (MAPMaxX - MAPMinX)**2)**0.5\n",
    "maxR = MAPdiagonal\n",
    "\n",
    "print(\"map has a total of {0} live counties for {1} districts\".format(nLiveCounties,nDistricts) )\n",
    "countyDensity = [0.00001]*nCounties\n",
    "\n",
    "for c in liveCountyList: \n",
    "    countyDensity[c] = countyPop[c]/countyGeom[c].area\n",
    "\n",
    "avgCountyDensity = np.sum(tractPop) / MAP.area / nLiveCounties\n",
    "maxCountyDensity = np.max(countyDensity)\n",
    "print(\"avgCountyDensity, maxCountyDensity are\", r3(avgCountyDensity),r3(maxCountyDensity))\n",
    "print(\"here is a map of log county density.  All district diameters should be less than\",r3(maxR))\n",
    "for c in liveCountyList : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    plotCenter(round( np.log(countyDensity[c]),1 ),countyGeom[c] )\n",
    "    plotPoly(countyGeom[c])\n",
    "plt.show\n",
    "\n",
    "print(\"I will now assign precincts to counties\")  #some states do not report precincts by county\n",
    "countyPrecinctList = [[]] * nCounties\n",
    "for c in range(nCounties):\n",
    "    countyPrecinctList[c] = []\n",
    "precinctCountyNo = [-999] * nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:  #remember, we may have sliced out some precincts\n",
    "        precinctCP = vtdGeom[p].centroid\n",
    "        guessedCountyNo = 0\n",
    "        foundCounty = \"no\"\n",
    "        while foundCounty == \"no\" and guessedCountyNo < nCounties:\n",
    "            if countyGeom[guessedCountyNo].contains(precinctCP) :\n",
    "                countyPrecinctList[guessedCountyNo].append(p)\n",
    "                precinctCountyNo[p] = guessedCountyNo\n",
    "                foundCounty = \"yes\"\n",
    "            guessedCountyNo +=1\n",
    "            if guessedCountyNo == nCounties and foundCounty == \"no\":\n",
    "                print(\"uh-oh. precinct center\",p,\"not found in any county !!\")\n",
    "                GUESSEDCountyNo = 0\n",
    "                foundCounty = \"no\"\n",
    "                while foundCounty == \"no\":\n",
    "                    if countyGeom[GUESSEDCountyNo].intersects(vtdGeom[p]) :\n",
    "                        countyPrecinctList[GUESSEDCountyNo].append(p)\n",
    "                        precinctCountyNo[p] = GUESSEDCountyNo\n",
    "                        foundCounty = \"yes\"\n",
    "                        print(\"but this precinct intersects county\",c)\n",
    "                    GUESSEDCountyNo +=1\n",
    "\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5523cac3-4fa1-43d4-9cef-6f100d8dc723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's check that we assigned all precincts, but did not double-assign\n",
      "nPrecincts,assigned, expected 2173 2053 2053\n"
     ]
    }
   ],
   "source": [
    "print(\"Let's check that we assigned all precincts, but did not double-assign\")\n",
    "totalCountyPrecincts = 0\n",
    "for c in range(nCounties):\n",
    "    totalCountyPrecincts += len(countyPrecinctList[c])\n",
    "print(\"nPrecincts,assigned, expected\",nPrecincts, totalCountyPrecincts,nPrecincts-np.sum(isSkippedPrecinct))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ae33f768-db63-4564-b055-75dff784d650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2.137895557097503\n",
      "1 5.4615923000000674e-05\n",
      "2 9.448061049996951e-05\n",
      "3 3.936641100000017e-05\n",
      "4 4.4911112000011707e-05\n",
      "5 0.00021583735900002434\n",
      "6 8.0590530500021e-05\n",
      "7 0.0001590823200000069\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polygon\n"
     ]
    }
   ],
   "source": [
    "#minor map triage for Massachusetts - some little islands to fuse to map\n",
    "counter = 0\n",
    "for geom in MAP.geoms:\n",
    "    print(counter,geom.area)\n",
    "    counter +=1\n",
    "    if counter == 1:\n",
    "        buffMAP = geom\n",
    "    if counter > 1:\n",
    "        plotPoly(geom)\n",
    "        buffMAP = buffMAP.union(geom.buffer(0.01))\n",
    "plt.show()\n",
    "\n",
    "plotPoly(buffMAP)\n",
    "plt.show()\n",
    "print(buffMAP.type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "67c73d9a-5a69-4c8c-a841-7c18dd5984f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "special for MA.  I will save the old map as nonBuffMAP before committing to the buffered MAP\n"
     ]
    }
   ],
   "source": [
    "print(\"special for MA.  I will save the old map as nonBuffMAP before committing to the buffered MAP\")\n",
    "nonBuffMAP = MAP\n",
    "MAP = buffMAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7122198a-1436-4c3d-9e3e-1ef4ccd63b92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 1620 tracts\n",
      "I am working on tract number 20 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 10.0 1 93.4 0.9928 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 11.0 1 93.4 0.9406 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 12.0 1 93.4 0.8912 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 13.0 1 93.4 0.8443 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 18.0 1 93.4 1.1889 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 19.0 1 93.4 1.1264 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 20.0 1 93.4 1.0672 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 21.0 1 93.4 1.0111 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 22.0 1 93.4 0.9579 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 23.0 1 93.4 0.9076 209714.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 26 24.0 1 93.4 0.8599 209714.4\n",
      "loop31.0, tr26,wedgePops784679.55, 195034.7, 199521.1, 194933.2, 195190.6, Overedge?0, 0, 0, 0, ,Satisfied?1011,yoyo?3822 \n",
      "   targetWP, latest drx4 are tWP,dr, 195274.8,-0.0003, 195274.8,0.3539, 195274.8,0.0008, 195274.8,0.0002\n",
      "I am working on tract number 40 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 48 8.0 3 67.7 0.5311 209509.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 48 9.0 3 67.7 0.5036 209509.2\n",
      "I am working on tract number 60 of 1620 tracts\n",
      "I am working on tract number 80 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 82 5.0 3 90.0 1.8099 200433.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 82 6.0 3 90.0 1.7747 200433.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 84 6.0 1 55.5 0.4694 242894.4\n",
      "I am working on tract number 100 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 119 7.0 0 89.9 0.5335 195800.4\n",
      "I am working on tract number 120 of 1620 tracts\n",
      "I am working on tract number 140 of 1620 tracts\n",
      "I am working on tract number 160 of 1620 tracts\n",
      "I am working on tract number 180 of 1620 tracts\n",
      "I am working on tract number 200 of 1620 tracts\n",
      "I am working on tract number 220 of 1620 tracts\n",
      "I am working on tract number 240 of 1620 tracts\n",
      "I am working on tract number 260 of 1620 tracts\n",
      "I am working on tract number 280 of 1620 tracts\n",
      "I am working on tract number 300 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 303 2.0 2 90.0 0.4481 207349.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 303 3.0 2 90.0 0.4282 207349.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 303 4.0 2 90.0 0.4092 207349.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 303 5.0 2 90.0 0.3911 207349.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 303 6.0 2 90.0 0.3738 207349.1\n",
      "I am working on tract number 320 of 1620 tracts\n",
      "I am working on tract number 340 of 1620 tracts\n",
      "I am working on tract number 360 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 371 3.0 1 75.4 0.4114 338032.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 371 4.0 1 75.4 0.2875 338032.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 375 3.0 3 37.4 0.4446 249776.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 375 4.0 3 37.4 0.4343 249776.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 375 5.0 3 37.4 0.4243 249776.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 375 6.0 3 37.4 0.4144 249776.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 375 7.0 3 37.4 0.4048 249776.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 375 8.0 3 37.4 0.3954 249776.5\n",
      "I am working on tract number 380 of 1620 tracts\n",
      "I am working on tract number 400 of 1620 tracts\n",
      "I am working on tract number 420 of 1620 tracts\n",
      "I am working on tract number 440 of 1620 tracts\n",
      "I am working on tract number 460 of 1620 tracts\n",
      "I am working on tract number 480 of 1620 tracts\n",
      "I am working on tract number 500 of 1620 tracts\n",
      "I am working on tract number 520 of 1620 tracts\n",
      "I am working on tract number 540 of 1620 tracts\n",
      "I am working on tract number 560 of 1620 tracts\n",
      "I am working on tract number 580 of 1620 tracts\n",
      "I am working on tract number 600 of 1620 tracts\n",
      "I am working on tract number 620 of 1620 tracts\n",
      "I am working on tract number 640 of 1620 tracts\n",
      "I am working on tract number 660 of 1620 tracts\n",
      "I am working on tract number 680 of 1620 tracts\n",
      "I am working on tract number 700 of 1620 tracts\n",
      "I am working on tract number 720 of 1620 tracts\n",
      "I am working on tract number 740 of 1620 tracts\n",
      "I am working on tract number 760 of 1620 tracts\n",
      "I am working on tract number 780 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 798 14.0 1 37.3 1.9032 272626.1\n",
      "I am working on tract number 800 of 1620 tracts\n",
      "I am working on tract number 820 of 1620 tracts\n",
      "I am working on tract number 840 of 1620 tracts\n",
      "I am working on tract number 860 of 1620 tracts\n",
      "I am working on tract number 880 of 1620 tracts\n",
      "I am working on tract number 900 of 1620 tracts\n",
      "I am working on tract number 920 of 1620 tracts\n",
      "I am working on tract number 940 of 1620 tracts\n",
      "I am working on tract number 960 of 1620 tracts\n",
      "I am working on tract number 980 of 1620 tracts\n",
      "I am working on tract number 1000 of 1620 tracts\n",
      "I am working on tract number 1020 of 1620 tracts\n",
      "I am working on tract number 1040 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1051 2.0 0 90.0 0.3839 359161.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1051 2.0 3 90.0 0.46 224340.7\n",
      "I am working on tract number 1060 of 1620 tracts\n",
      "I am working on tract number 1080 of 1620 tracts\n",
      "I am working on tract number 1100 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1117 4.0 1 58.0 0.2589 274282.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1117 5.0 1 58.0 0.2297 274282.8\n",
      "I am working on tract number 1120 of 1620 tracts\n",
      "I am working on tract number 1140 of 1620 tracts\n",
      "I am working on tract number 1160 of 1620 tracts\n",
      "I am working on tract number 1180 of 1620 tracts\n",
      "I am working on tract number 1200 of 1620 tracts\n",
      "I am working on tract number 1220 of 1620 tracts\n",
      "I am working on tract number 1240 of 1620 tracts\n",
      "I am working on tract number 1260 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1279 2.0 0 90.0 0.4657 223081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1279 3.0 0 90.0 0.4207 223081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1279 4.0 0 90.0 0.38 223081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1279 5.0 0 90.0 0.3433 223081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1279 6.0 0 90.0 0.3102 223081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1279 7.0 0 90.0 0.2802 223081.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1279 8.0 0 90.0 0.2531 223081.9\n",
      "I am working on tract number 1280 of 1620 tracts\n",
      "I am working on tract number 1300 of 1620 tracts\n",
      "I am working on tract number 1320 of 1620 tracts\n",
      "I am working on tract number 1340 of 1620 tracts\n",
      "I am working on tract number 1360 of 1620 tracts\n",
      "I am working on tract number 1380 of 1620 tracts\n",
      "I am working on tract number 1400 of 1620 tracts\n",
      "I am working on tract number 1420 of 1620 tracts\n",
      "I am working on tract number 1440 of 1620 tracts\n",
      "I am working on tract number 1460 of 1620 tracts\n",
      "I am working on tract number 1480 of 1620 tracts\n",
      "I am working on tract number 1500 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1511 18.0 1 46.2 2.3294 263503.0\n",
      "I am working on tract number 1520 of 1620 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1534 4.0 0 104.4 0.4907 197197.4\n",
      "I am working on tract number 1540 of 1620 tracts\n",
      "I am working on tract number 1560 of 1620 tracts\n",
      "I am working on tract number 1580 of 1620 tracts\n",
      "I am working on tract number 1600 of 1620 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.0001081635117117\n",
      "Average and max number of wedgePop loops per tract were:  6.241975308641975 31.0\n",
      "min,average,max of Home District areas were:  0.0 0.2210405503648372 0.9891783539897296\n",
      "calculated statewide vote was 0.32771238350761006, should have been 0.3288445045985185\n",
      "calcd statewide Hispanic pop was 0.10811919378638106, should have been 0.10973047234124975\n",
      "calcd statewide Black pop was 0.06577382498270219, should have been 0.06616284023777293\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.37962962962962965\n"
     ]
    }
   ],
   "source": [
    "#3/13/22  HD1.  (from HD2, but using coeff1, coeff2; unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "# 8/10/22 - pivot from using \"grids\" to counties for subdividing map\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi #adjustable parameter.  (avoid wedges too close to half a pie)\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    else :  #viable tract, proceed with preliminaries\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "        maxStartDensity = max(avgCountyDensity,countyDensity[countyNo[t]])\n",
    "        minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "        tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "        angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "        loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "        loopPrecinctUse = [0.]*nPrecincts\n",
    "        # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "        HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "        nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "        targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "        wedgePop = [0.]*nWedges\n",
    "        oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "        wedgePopGap = [0.]*nWedges\n",
    "        isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "        isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "        yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "        wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "        wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "        currentR = [0.]*nWedges\n",
    "        old_dr = [0.]*nWedges   \n",
    "        for nW in range(nWedges):\n",
    "            currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "            old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "        oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "        max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "        for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "            angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "            angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "            pt1[nW] = tractCP\n",
    "            pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "            pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "            wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "            wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "        HDpop = np.sum(wedgePop)\n",
    "        \n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    #print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts that could straddle multiple grids (though unlikely)\n",
    "                for nG in liveCountyList :  # loop over ACTIVE grids (counties) to look for intersecting tracts\n",
    "                    gridIntersxn = countyGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to (intersects with) this wedge\n",
    "                        for nTT in countyTractList[nG] : #look for intersxns with all tracts in this grid\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    #print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  #below line adds guardrails\n",
    "                            wedgeAngle[nW] = max( (totalLiveAngle - maxWideAngle)/(nActiveWedges-1)  , min(wedgeAngle[nW], maxWideAngle) )                                \n",
    "                # NOTE.  On 8/10/22, we tried an alternate wedge-angle split scheme for MA but it led to wider tractUse distro                \n",
    "                else :\n",
    "                    dualShortedWedgeType = \"opposing\"\n",
    "                    #print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in liveCountyList :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = countyGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for nTT in countyTractList[nG]:\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in liveCountyList :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = countyGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for nTT in countyTractList[nG] :\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for nPP in countyPrecinctList[nG]: #scanning the list of precincts in this active grid/county\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection (though unlikely)\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct        \n",
    "        \n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)\n",
    "#start 8:56, end 9:34"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "bd00278a-cfec-45d1-9478-7097d5c13504",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "21208d03-647d-4474-805b-cb51027c1fb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MA 12Aug\n"
     ]
    }
   ],
   "source": [
    "date = \"12Aug\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t\n",
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7e81102-00ed-48cf-a4cd-5d4298434b2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# OPTIONAL - READ DATA BACK IN IF WE STOPPED AND RESTARTED\n",
    "infilename = \"OH15HD1tol0.005nW413Mar.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "tractPop = df2['tractPop']  #these four may include shifting demography out of corners\n",
    "#tractVAP = df2['tractVAP']   #OOPS!  I forgot to write tractVAP to all my state HD output files!!\n",
    "#                             Need to go back and add tractVAP after clipPoly slicing operation\n",
    "tractHisp = df2['tractHisp']\n",
    "tractBlack = df2['tractBlack']\n",
    "# ... and now the true outputs\n",
    "HDvPop = df2['HD-pop']\n",
    "HDvHisp = df2['HDvHisp']\n",
    "HDvBlack = df2['HDvBlack']\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "HDangle0 = df2['HDangle0']\n",
    "HDangle1 = df2['HDangle1']\n",
    "HDangle2 = df2['HDangle2']\n",
    "HDangle3 = df2['HDangle3']\n",
    "HDradius0 = df2['HDradius0']\n",
    "HDradius1 = df2['HDradius1']\n",
    "HDradius2 = df2['HDradius2']\n",
    "HDradius3 = df2['HDradius3']\n",
    "startAngle = df2['startAngle']\n",
    "\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractNo = df2['tractNo']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c3bfa446-0849-42ff-a6a9-f582127eb376",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is a sampling of the Home District leans across MA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is a sampling of the Home District leans across\",STATE)\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "plotPoly(countyMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eccf0ef3-715b-45ed-bb52-8625314cf612",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in MA\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "plotPoly(countyMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "383c447b-7933-4e8c-8276-dde081836627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.3 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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OI6AQ4nbgdoCUlJQGitt8lmTl0TNBlX9UKAJJrDedetfYMN65+UyVzyfInFQBCCEmA4ellKuEELY6jv8ZcAMf1nOJ0VLKHO8Av0gIsVVK+XNDBfQqjDkA6enpsqH9moPLo7FsVwGXDe/aGrdTKE57ftx6mCVZeXy0fC8An9x+Nt3iVO7+YNOQGcBo4BIhxEQgFIgWQnwgpfytEOJGYDKQIaWsc3CWUuZ4vw8LIeYDZwE/A7lCiCTv238ScDgQPygQ+Mo/qvq/CkWzyC+tYuKLv5BbXFWjXcVVtA1O6gOQUs6QUiZLKVOBa4AfvIP/RcCDwCVSyvK6+gohIoQQUdXbwAXARu/hL4Ebvds3Al8065cEEF/5R+UAVigaTUGZk0qXB4CXfsjyDf6XD0/Gfr+NV64dTnIH9fbfFmjOOoCXgRB0sw7Ar1LK3wshugBvSiknAonAfO9xE/CRlPI7b/8ngM+EELcCe4ErmyFLQFmalc+gLjHEhqt0swpFQ7FvO0xZlYeH52+gqMKFECAljO+fyNNXDCE8xEiIyUiqsvu3GRqlAKSUdsDu3a4zcNdr8pno3d4JnFHPeflARmPu3xqUVblZvfcot45R6R8Uioay5WAxN72zokZbtVF4xsR+dFC5+9skaiVwLZbv9pZ/VPZ/haJB/LjtMDf7Df6/O7cnD03oR4XXDBRuUcNMW0WluKzF0qw8LEZV/lGhaCh3frC6xv4bP+8kv8xJuMWkBv82jlIAtViSlc/w7rGEWVT5R4XiZHy/OZdKt4dzeidwjV8Gz3Of+pHPV+0PomSKhqAUgB/5pVVsPliszD8KRQNweTRm/m8TfROjePPG9BovTeVOD/f/ax278sqCKKHiZCgF4Idjp7f8Yx+lABSKk7EkK4/9RyuYPj6NULORgV2Or5rndGtBkEzRUJSBzo8lWflEhZgYoso/KhQnZeshvWxjdcz/FSOSGZYSS3SomdV7j2IxGujbWSV5a8uoGYAfS7LyGNkzTpV/VCgawEUDOwPwly824sjWZ8+9OkbSMSqECwd2Zlw/leO/raNGOi/7CsrZW1Cusn8qFA0kNSGCGRP6UVzp5uZ3l+PRWiVVlyKAKBOQl6XZKv1zU3A4dmK378Bm64PV2jPY4ihaCLdHo9KtERlSc8j43dhefLpiHwcKKzAaaib48WiSA0cr0KREAgYBKXHhJy2wVFzpYmlWHpUujUqXx2diumxEMtGh5oD+rvaOUgBeFmfl0zEqhD6q/GODcTh2kpHxIk6nG4vFRGbmH5QSOE2Z9OJidueXsfWxixBCsGpPAX+ev5EBXaJJjgtnZ14ZJZUuovwG6L/9bxPzHHtqXOeRSf25bUz9fyMeTXL9W8tZt6/wuGOpCRHY+iqzUiBRCgCQUuLIzuOc3gmq/GMjsNt34HS68XgkTqcHu32HUgCnKdtydYdvjxnf8MGtI1m15yhbD5X4HMEAOYWV9O18TAGs319Ev85R/G5sTwSCP/5rHQVlzuOu7c/7jt2s21fI36cM5JzeCZRUupnyyhIGJEUzpk/Hlvlx7RjlA0D/484rdTJKmX8ahc3WB4vFhNFowGIxYrP1CbZIihbiL5MH+LZ/+9Yynv9++3Hn3PreCp+5BmB3fhkjundg6rBkLh3WFaNBcCI3wcGiCp5ZuJ0xfRK4/uzu9OwYyes/ZWMxGXjxN8OOMzEpmo9SAMDiHcr+3xSs1p5kZv6Bxx6brMw/pzk3WLsz67LBx7U/PLEf3eL0qnn7j1aQU1gBwNEyJ4XlrhoVvwxCn23Xx8wvN+HyaPzj0sEIIfhmw0G+3XiI6eP70FuZZlsEZQIClmbn0yMhgq6q/GOjsVp71jvwOxzZ2O3bsNn6YrWquq+nMiajgd+clYK1Zzy2Z+wAvHztMCYNTuKc3h3500fzyKuIZtePX7OzpIDYjKsBaikAgVaPAli46RALNuXywEV9SYkP52iZk798sZHBXWO4/QQ+A0XzaPcKwOXRWLYzn0uHqfKPgcThyCYj41k/B/EflRI4hVm15yj3frqWg0X6G36ExchtOQch/zCzk3Zy3/DnEZqJtO/fZOnhFeyT3wKxNXL/6wrg+GuXVrn565d6Solp3sH+sa82U1juYt4tI9W6nBak3T/Z9fsLKXN6lPknwNjt22o5iLcFWyRFM9iUU8TegnJcHkliVAhlTg8hi3PBrbEl52sAQnNGAhBuiubAjhyMBkE3v8pf1QViavPcwu0cKq7kn5cNxmw08OPWw/xnzQHuHNebAV2iW+X3tVfa/Qxg8Y58vfxjT1X+MRA4HDuw2zcTHx+NxWLC6fR4HcR9gy2aohlcPjyZv3yxCYDckmP1fXuL7ZyBng46c0834suP4JED+cYdxpm9OmAxHXvHrMsEtGF/Ee8u3cV1I1MY0b0DxZUuHp6/gbTESO4eV2fNKUUAafcKYEl2HgO7RKuKRQHA4dhBRsY/fWaf2bNvID+/QvkATgPe/3XPcW3u3EquOfouMfHFbNl8Lh6PmbciBGcM6cqTY3sel1VX1HICuz0aM+avJ9pixLR5J47EUL48WElucSWv/XZ0DeWhaBnatQIod7pZs/cot4xW5R8Dgd2+2Wv20XA63eTnFzNjxpRgi6UIAP4rcCeERzHvvU2U7yznkXVXE3uevjird6dIPrp2+HEJ4ByO3djtWXjcGt9vOcy+oyswCCiucLPxQDFF3zh4fPMenu+eSIcrx3HbOT0Y2i22NX9eu6VdK4DluwpweaSy/wcIm22A1+yjzwBstgEn76RoUzgcTux2JzabBav12Kz4okGdeXj+Bq5O78alXRN474k1GI0C58ZiPnj+AoafmVhn9S+HYzcZGa/hdLqJOy+NSGsq63fkERFpISzMzGCLxreb9+DxaGih+v3OUenYW412rQCWZuer8o8BxGrtQ2bmw9jtm7HZBmC1qoVhpxIOh5OMjAKcTrBYIDMzzqcE4iIs7H5iElJKhBBkZl6J3b4Pm60b4OKFZ3/GZtPNfHb7Tmy2nlit3bHbs3zBAPmZWRTaD6Bp0hsZdjNQReYz+kuDduAIRgGO7HyV8qGVaLACEEIYgZXAASnlZCHE08DFgBPIBm6WUhbW6tMNmAd0BjRgjpTyBe+xmcA04Ij39IellN8069c0kiVZeQxLUeUfA4nV2kcN/KcodrsTpxM8HnA6Yd68Eux2ic0WjtWqR/NUp0qxWrsAgnnz1vHOOz/jdnswmYxICR6P5h3gp2Gz9fbNCoUw4fFINE3idLqx23cxY8bYGi8Nr2wqIHPrYWZM7B/EJ9F+aIyX5R5gi9/+ImCQlHIIsB2YUUcfN/BHKWV/4GzgLiGEv13geSnlUO+nVQf/gjInm3KKlfmnhXA4tjNr1nwcjuNTBijaJjabBYsFjEYwGt28884RHn30CBkZe3E4ymucO2fOJsaO/Zw33lhJVZWGx2OkqkrD5fJ4Q3/d2O07sVpTmT17KhkZ/bjvvnMJCTFhNAqviVD3vVmtfZgxYwpWax/O69eJrMOl7MlXpSRbgwbNAIQQycAk4B/AfQBSyoV+p/wKXFG7n5TyIHDQu10ihNgCdAU2N0/s5lNdwEIpgMDjcGwnI+Mxv0Vgj2K1pgVbLMVJsFotZGbGYbc72bu3jLlzJR6Pk8pKJ/PmHcFq7Y7DcZh587KYO3c9Ho8RiAOMQDlgQgg3QmiYTAb27s1lzpylTJ++AKfTzS+/7Gf27Mnk55djs/XAak2pcX+HYyfbvetFMrcc5pZzVHBGS9NQE9Bs4AGgvvputwCfnugCQohUYBiwzK/5biHEDeimpT9KKY/W0e924HaAlJSU2oebzJLsPCJDTJyRrMo/Bhq7fVONaCC7fZNSAKcIVqvu/HU4DLz9di4eTwFSwjvvlBMdXcRzz63D7XahDx2h6JP8aGAfUIGUCRgMRjyeHObO/REhItC0MJ/ZJz+/jBkzbEDNWhIAGRmzcTrddL7pXN7/ZSc3j05V2XlbmJOagIQQk4HDUspV9Rz/M/pfwYcnuEYk8G9gupSy2Nv8GtALGIo+S3i2rr5SyjlSynQpZXrHjoFLB7skK4+RPVT5x5bAZhvolyXUhM02MNgiKRqJ1RrOLbeEUT3+ulwVPPPMOtxuDf2NXwBF3s8hIAQwImUsmtYJjyfaa+93YjQKP7OPnupBryXxEo8++hUZGS8xb94yn7O4ZO0edhVVsraOmgCKwNKQ0W80cIkQYjfwCXCeEOIDACHEjcBk4DpZT5o/IYQZffD/UEr5n+p2KWWulNIjpdSAucBZzfoljWD/0XL25Kvyjy2F1ZpGZuajPPbYVcr8cwpzww2dCA01YDBEAyloWjxgAarz+7iBMo4NI9GAASk1jMZyDAYDBoOHe+89k8ceO5/MzFt9Zp/atSRA+F4aqrbmYpSSZ7/c1Lo/uB1yUgUgpZwhpUyWUqYC1wA/SCl/K4S4CHgQuERKWV5XX6HP394Ctkgpn6t1LMlvdyqwsYm/odEszdLt/6N6q/QPLYXVmsaMGVPV4H8KY7XGMHv2CIzGYehxHCPRbf5RQDi6AhCACz3ITwKFCLGXyZPTMBoNaBq89NL3xMcbsNs34HBkAcfXkrjhhrPIzJzOtGljwG2hcM0BftlzlG9/zArKb28vNGcdwMvo875FXjvdr1LK3wshugBvSiknos8ergc2CCHWevtVh3s+JYQYiv5Xsxv4XTNkaRRLs/NIiLTQN7E+l4aiKTgc2/3WAARv4Hc4tmK3b8RmG4TV2i9ocpwO5OdHoml6EjchipCy2iYfjj74e7wfF/pwcBQpqygoiELT9JDPqio3d931EVJWBwXcj9Xam8zM/2PevBXe6xiwWlOx23fhdnsoXnWAqOHJvG3PZoLKCdRiNEoBSCntgN27Xee/ipQyB5jo3V6M/q9b13nXN+begUJKyZLsfKy9VPnHQKJH/jzuF/nzSFCUgMOxlYyMv/jJ8XelBJpBfHwBBkMsmnYEIfL9cvmEkJoaxe7d/pP/MkB38S1Zko3RaAI8GAzg8bi8jmA9M6zV2hsw8N57q3E63bz33ioyM+8gPj4MITxohaU49x4lO60jLo+GWfnqWoR291Szj5RypKSKUb2U+SeQ1M4DZLcHJ9LXbt9YS45Wsyyedjgch5k+/X+43V8g5Tdo2logB33t5i52764O2pPej8vXV0rJxInDyMgYyr33TvDG/xtqZIb1XyXsdLqZN28506f/C01zYTR6uCo9mYJKN+v3F7bmz25XtLtUEEu89v/amQoVzaOt5AGy2QbVkmNQUOQ4HbDbD+J0aujR2R6gyvsB/W3fybEJvkCPDjrGN9/k4vGY+OWXUmbPvpn8/Dzi4xOx2/XIIf9VwhaLCdAVgaZJhJBEVlQCsHZfESO6q3QtLUG7UwBLs/NI7hBGSnz4yU9WNBg98ueRoPsArNZ+ZGb+XfkAmonDcYi9ewsxGDQ8HoHu5K3GSbWpR8eCbkwwoysBD3379mT79ipflE9+vsRmG0FGxhe+GhGZmVPIzLwDuz0Lm603oPHee7/idHowmQzk7z9KQuc4Vu89yq2oRWEtQbtSAB5N4sjO56JBnYMtymmJ1ZrWJqJ+rNZ+auBvBg7HId9ArWNBH9irTTxOX3UvgwH69YumvNzD7t1F6IogiosvHsRLL631KwjUDbv9AE6nx69K3AFmzBiB1Zrqu7fuGF7O228vZe7cxSRcPAyPxJeEThFY2pUC2JxTTHGlW8X/KxQnwH+gNhjAZAK324Me9mlAiBAMhjI0TcNoNJCVlYPL5eSYH6AUu30Zs2fbyM8X2GzdsFq7AkYsFqNPKcTHS2bN+gqbrZ/XKQxWa0/s9u14PPr9K/YWcLRvElmHS+mjovYCTrtSAEuy8wBV/lGhOBE2W1csFiNVVW6MRsFVV3Xlk0+W4fHoZiCjMQYhpDfM04mULr9avwbAzYoVO9mwYTeZmX/yDv5gtXYmM3MKdvsB4uMl06fPxel0YbGYvef19t4/zVdO1L1bTxH96Yp9PDJZ1ZcINO0qCmhpdj59OkXSKTo02KIoFG0Wq7Uzs2dbMRgq8XiK+PTTFb7wTyEgLc2E2+1BSomUYDQaMPhGEl1JSKn5Qj4djp3MmvUdDsdOrNbOzJgxgvz8QzidLr/MoVv97t+T2bOvJiNjIM/PmsqFgzrz79X7qXJ7UASWdjMDcLo1Vuwq4Kr05GCLolC0efLzi9G0ct8iMIPBgxAujEYLO3YU+N74zWYL99wziueeW4CULgwGAUg0DUwmA/HxUWRkPO+3LuNerNae2Gz9sFjMftFa/XA4dmG37yA+PpLp07/0ZhDdwwuf/JZvyl2879jDbWN6BvW5nG60GwWwdl8hFS4Po5T9X6E4KfHxoWiaPspL6UKIckBDShceTxh66mfJhAl9WLs2C02r9M4IwhAiDj0zaARr1hyukfPHbt+O1drTuxL4T9jtW7HZ+gFGMjJexul0YzAIPB4DmiZwOt0c2XiIMzqF89TXm+nqrmDCOJVcMFC0GwWwJCsPg4Czeyj7v0JxMvLzSzEYnGiaQIgqPB4NKUHTNKR0AyFIKfjqq+VoWiWaJjAYBAZDOJoWgpRGXC4Phw5V+uz5RqObvXv34XBs9UZq9fbZ/WfNWuhTFPqMQyCEwGQysGLFBr7/dSsJ103illd/4d8hRkaNUlFegaDd+AAc2fkM6hpDTLg52KIoFG0em60HISEGjEYPZrMJi8WEwSC8voBS9HUAebjdlWiaxGAQpKenMnlyL4xGDShDykq+/XYjs2f/hmnTzkKIQubO/Y6MjL/icGytdb9jyeFCQky88srlTJuWjpSH+eKLpZQfyufoz6sI6dWNNxZtrUNiRVNoFzOAcqebNfuOcus5yn6oUDQEq7U7mZm3Yrfv8pZu1Jg+/V8sX56N7uitojoDqBACs9nE2rXbWbVqExCPEGFIqYeP5udXkJIShdvtqVEkCEzY7Vuw2fp7TUJ3+wrEWK09mDUrF4+nwueALl25mci07ixP7cy+gnK6xanFnM2lXSiAFbuP4vJIlf9HoWgEVmt3rNbuvv0uXSLQB/3qjx6VI4RgwoTBfPmlA01zAwUYDF0RoroITC+gskbah/j4GDIynvRzDj/oNQkdW/F7LL2IbhIaNqw3V1hTeO+wh0e/2Mi7N7daCZHTlnahANbs1ZNWjejeIciSKBSnMkb0xV4e7/cxCgpK0DSnd68STTuA0RjB7Nk3+Fb6Zmb+Dbt9EzbbQL+CMNUzgi0+f8AxQrnxxos5dOgg33yzhVWr8tiw4b/c+uJ1fJeVR4XTQ5jFSLCodHn4cm0OCzYdYmdeGaVVboxCkBIXzoAu0UweksSI7h3a9ArmdqEAkmL0uP/c4kp6dowMsjQKxalJ585R1B749ZBPyeLFtbOuVqFpTvLzj/haaqboMNVK2te/Rm+HYw8ZGW/hdLoRogpNM/jSSbtz9Bn9mr1HgxbVV1Dm5Nq5v7L1UAkpceEM6hpNTJgZl0eyO6+MT1bs5d2lu+kaG8aFAzszaUhnhqe0PWXQLhTA4K6xAGw4UKQUgELRRG644WzeeutnXC69hOOx1A8eNO34RVq6qSeaWbP+460LLfxs/mlkZj5Ywwfgj92+yxcVZDAYMBoNCCGxWIxcYevNgm938OvO/KApgL//bxPZR0qZe0M64/t3Om5gL61ys2DjIb7deJAPlu3h7SW7sPaM56+XDKBf5+igyFwX7UIB9EmMJMRkYP3+IqYM7RpscRSKUxKrtRc//fQA06d/wPLl29AdwR5Aw2AwIKXAYDAwenQ/BgzoxrBhvZg+/W2cTjdGowkhzLjdGiaTgZtvHsMNN5zLjBkX17iHw5GF3b6N+PgYvxlCGLNnX0p+fjE2WxpWay9GbM7jq/UHmT4+zbv4rHX5dWcBKXHhnNUjrsbg/9P2I7z8ww4OFVdS4fTQPyman/5kY8HGQ7yQuYNJLy7mtnN6cN8FaYSYgme+qkbUU8u9TZKeni5XrlzZpL5TX13CwcJK0lM74NEkLo9ESonJKDAZDViMBkwGgdmkb4eajYSZjYRbjIRajISbjYRZvB9ve5jZf99EqNnQ5qZ4CkWgcTi2ce65f8btdlPtDBYCDAYDF188kgceuByrtT+zZv2HRx/9GI9HQwgTYPSuIJYIoREaamD27NvIzy/zmoAEGRlP+8xCs2dfT36+2xeFVJ022mrtwX/XHGD6p2t568Z0MvontvozmPvzTmZ9u4UeCRH8547RxISb+XVnPtfM+RWAXh0jyD5S5jv/qcuHcP6ARJ78biufrNjHGd1iefW64XSNDWsVeYUQq6SU6ce1txcF8P6ve5jzczZmgwGjQR/0DUJPEe30aLg9ErdHw+mRuDwaFS4PTrd28gvXwl8phFm8CqROhVG9byLMbCDEbMRsNGA2CixGg75tMmA2CIzej8EgMBkEBiEwGQVGUXdbbLgFi+nYEg8pJeVOD25N4tEkbk1D0yAmzBxUJ5ri1GXOnIXcffcbuN0upDzmFBYCQkMtZGbOAgxkZMysMQNwOt3esE6X17QTjqZJLBYTN954LnPn/ozHIzEaDTz22FRmzJiEw7GLjIxX/SKG7iT9rO6MfepHkuPC+ex31qA8g6XZedz09grSUzvw4W0j6THjGwD+dGFfrvdGTz27YBvrDxSxZm8hj106iOvP7s53Gw9y/7/WExdh4b93jSYuwtListanANqFCQjg+rO7c/3Z3U9+oh8eTVLh8lDh1D/lLrdvu8Llodz77b9f6fJQ7nRT4dSocLn1c5weCsqc+jV85+h9WoL07h34/I5Rvv2H52/k4+V76zw3NtxMUkwYSTGhJEaHEBtuITbMTIdwC1OGdcFiNLD1UAkbDhSx6UARBworEQKMQldKQqArJ6F/jAb0bYNXQQk9hM/gPd/gbTN6V3oavX2E7zg1z/W2VSu7EJOBcIvJqzyPzdRCzUZCTLoiDTHpszk1G2s5br/9AgYPTmHevB94552FOJ0uX3K4qio3M2d+xMyZ15GZOdMX+QOCefN+5u23M/F4BEKY8Hg0r3NXn01Urxr2Lx05b95KKiv1lch6OoksrNYe3HJODx7/egvbDpXQt3Prp4oe1SuBByf047GvNrM0O9/X/vSCbfx71X5+uN/G36YMotLl4db3VvDofzfSu2MkFw1KIjE6lKvn/Mrv31/F+7edFTRzULuZAbRFpJRUujTKnW6cHg2XW5+NuLwzkuptTZP627uUvm3ft6x+q9fb3nPswe3RWHTfWACKyl2c8feFANw7Po3YcLNvcD1a7uRgUQUHCyvJKarkSEklheUu3N4cMNGhJnp2jGTtvkIAwi1GUryLbzQp0SR6SmBZLRs+eTTpv63L5qnVR2vBPz2DAIvJgNlgwGQU3tlVzW2L/7ap+vtYW41zTAaGdovlwoGqmFBt5sz5hrfeWsiaNTu9A7pBV9YhZjIzHz+uOI/DsQ27fRPx8TFMn/6R35v9Q+iO4m3YbH2xWnvjcOxh3Lg3qKrSi9FYLGC334nV2oNDRZWcPSuTGRP68buxvYLwy/VQ0POesRNqMXLtWSk8/vUWQJ9dr/vrBb7zyp1uxjz5I8O7d2DuDfqL+JfrcvjDx2u4/uzuPHZpy5YubfYMQAhhBFYCB6SUk4UQTwMXo9eHywZullIW1tHvIuAF9CDiN6WUT3jb44BPgVRgN3CV1IuPthuEED6/QqD43/ocKl3HTFdvLdnl275nfJ+T9pdSUlzp5tH/buRgUQVlVR4entiPjP6JpMZHYAygw03KmopCSvD4to9XJB5NUuXWqHTVnHlVOD1UuT1UuTWqXNqxbbeuQKsVqsujm7+qlavT73h5hQeX377LI2v0r3R56BBuOakC0DRJpVuXqbJaVq98lS7N237seJXLQ3GFi6IKF8WVboorXBRXuqhwebwya7g1qZsoNc2n7KWEJy4bzITBSQH792gKDsdmpk9/HafThdFoIj09jZUrs31v9Xb7huMUgNXaF6tVf7sfPDjFLxKoj/f4sYgguz3bW4xGn23ecstI32KxzjGh9OscxU/bjwRNAYSajTx/9VCuf3s5b/6yi4TIEPJKq5h12eAa54VbTFx9Zjde/ymbg0UVJMWEcckZXViz9yjvLt3NlenJDEmObXX5G2MCugfYAlTHMC0CZkgp3UKIJ4EZwIP+HbxK4xXgfGA/sEII8aWUcjPwEJAppXxCCPGQd79Gf0XjKal017ApVjjdAPzfebUX2dSNEIKYMDMv/mZYi8hX+15GAUYE5jbuinh6wVZetWdz49vLqXR5B3f/wdzb1hS/kRAQFWIiOsxMTJiZqFATnaJC9aAE76zFZNDNWiajbh6b59jDttySoCsAu329N6+/B03T6NIllpAQ/zTPg0/Y32rt4xv44VgUUPUMwGbrVWO9wA031HyJPad3AvO8s16TMTipzUb2jOfdm87k7SW7AMHUYV2ZWMe/y2XDu/KqPZvnFm7n6SvPAOC+89P437qD/PXLTfznjlGtbrZskAIQQiQDk4B/APcBSCkX+p3yK3BFHV3PArKklDu91/kEmAJs9n7bvOe9B9hRCqDZlFS6OVru5Lb3VpB9pIxdeXokwg1+dVcVjWd07wR+2HqEo+VOQs1GYsLMdI4OqemDMBt82/q37qMIrdUW5tcWajYQ4U201lCklMxz7GnBX9twbLYhCGEA9AIx3367jBdfvJv8/FJstsG+t3+HYwt2+3pstiFYrf3rvJbDkUVGxjN+JqH7vTmCbsduz8Zm61UjNQXA8O4deHPxLuY59nDLOcErHD+qd8JJ1yT07qT7KRZuzuVpb1tUqJkHLurLA5+v54u1OVw6rHXD1Bs6A5gNPADU52m5Bd2cU5uuwD6//f3ASO92opTyIICU8qAQolNdFxZC3A7cDpCSktJAcdsvcREWNuwvItxson9SFJOHJDE2rSMdo0KCLdopzaheCXx7z5hgi9Em0f2IugJzuTzk5xcyY8ZvfMcdji1kZMzwG9hn1akE7PZttWoHbPPmB+p+3MBfzUUDOzM2rSN//2ozR0qrePCitp0mesrQLnyxNqdGkfsrhifzvmMPTy/YxqQhSZhbcSZzUgUghJgMHJZSrhJC2Oo4/mf0atEf1tW9jrZGuf6klHOAOaA7gRvTtz1SHRIXSFu9QlEfdvv6GgpACMHevYdxODZjtQ7wnVMz78/6OhWAzdYXk8mIprkxmQy+KCDdLLS1RvH4agwGwVs3pvPoFxt5zZ5NWmIkU4e13ap/1QEWeaVO30uZwSC49/w+3PLuSr5cm8PlI1pP/oaomtHAJUKI3cAnwHlCiA8AhBA3ApOB62Td4UT7gW5++8lAjnc7VwiR5L1OEnC4Sb9AUYPqNQMKRWtgsw0hJMSih/0a9VDduXO/JSPjQRyOzb5zqnP9636BITgcW5g161Mcji1+V9OQshJweb81r1noaR555CPOPXcGc+YsOE4Gk9HA36cM4qwecTz07w1szy1pld/eFMb11Q0dlbVCwMf17UTfxCje+DnbV4mtNTipApBSzpBSJkspU4FrgB+klL/1Rvc8CFwipSyvp/sKoI8QoocQwuLt/6X32JfAjd7tG4EvmvE7FApFELBaB5CZOYvHH7+RadMuQtM075u+C7t9nfec/mRmzuKxx673LhCDjIwZPPro+2RkzPApAbt9Mx6PCymdeDwu7PbN2O1bqaoqQdMO4Xbncffdz+BwbDpODrPRwCvXDifMYmTml8cfbyvUF3YvhOAOWy+255byw9bWexdujrHpZXSfwCIhxFohxOsAQoguQohvAKReO+5uYAF6BNFnUsrqf50ngPOFEDvQo4SeaIYsCoUiSFitA7zRPhomk9H7pm/GZjvD75z+zJhxNVZr/zpNQnAs/7/RaMBkMrB3bw7x8SEYDC6qLccej4bdvrZOOTpGhfB/5/VhaXY+q/e2zYjybzceAsBkPH6WPnlIEl1jw3j9p+xWk6dRCkBKaZdSTvZu95ZSdpNSDvV+fu9tz5FSTvTr842UMk1K2UtK+Q+/9nwpZYaUso/3uyBQP0qhULQeDscmMjL+yNy5XyGlm2nTJpCZ+aTPB1CbukxCgDdD6CNMm2ZDylLmzv0f06e/xH33TcRsNmEwGAgJMWOzDa1XlmvO7EZ0qIk3f9nZEj+12RiEni6mc3ToccdMRgO3n9uTlXuOsmJ36wyH7aYmsEKhaBns9rXetQAaHo+blJSEegd/ON4k5O8QtlrTSEmJxuOp8pmSYmPD+Omn2Tz++C1kZj6L1Tqw3mtHhJi47uzufLfxEHvz67NMBw+nR9KrU0S98f5XpXcjNtzM+60U5qsUgEKhaBY221AsFjNGowGj0cjevbl12un98TcJHX+9IVgsZgwGfZFcfHwUVutAZsy47oSDfzU3jUrFaBDehVlti54JEWw/VFqvozfMYuSCAYn8uO0wLk/jFxU2FqUAFApFs7BaB5KZ+SzTpk1CCMncuV+RkXHfSZUAgMOxgVmz5uFwbPC73gBmz/4dRiNompvp01/1RRQ1hMToUC45oyufrthHYbnz5B1akW5x4Tg9GjsOl3L2PzPpOeNr7v10bY1zzh/QmZJKN8t2trwZSCkAhULRbKzWgaSkJOJ2e/yigNaesI/DsYGMjD/w6KNzyMj4Qw0lkJ9fpCcN1GSDrlWb28b0oMLl4ZMV+05+civSIdwMwMYDRRwqrkSTMH/NAcq9KVtAT28RajawcPOhFpdHKQCFQhEQ/E1BJpPRuyCs/lmA3b7G5zvQB/k1dV5Ljyga2ihZ+idF07tTJKv2tK1ooOhQXQHMmL+BCIuRRyfrvpK3fjlmrgqzGDm3T0cWbc6tN2w0UCgFoFCcouzJL2fR5lxe+TGLFzN3tPhgcTKs1oHMnn0XI0akoWkwd+7XZGTcX68SsNmG1Rrkh/ldawCZmU/z2GM3kZn59AmdyvXRIyGizTmCY7wzAKdbY9KQJG4ZnUpqfPhxM5Xx/RM5WFTJjsOlLSpPuykIo1Ccbsxfc4D5aw749q8bmUJ8ZPByPjkcG5k+/UUqK91Iqb9bVi8Iq8t5a7UOJjPzRez2Ndhsw7BaB9c6PqBJA381USEmyl3uk5/Yivhn6o2PDEEIQajZyO78Ep76biu3nNODhMgQrL3iAXBk55OW2HLFbpQCUChOMYQQvHztMMqrPPRJjOR/6w7yztJdxIa3fGnB+nA4NjJz5ttUVTm9dX8FQhy/IMzh2ITdvhabbShW60Cs1sHHDfyBIsRsoMrV8pE0jcHjF/0zIEnPrP/3KYO46g0Hr9qzMRsN3Ht+Gt3iwknuEMbS7DxuHJXaYvIoBaBQnIJMHtLFt716byFSQl5pFYl1LDAKNA7HZr/UzgNwODaSkXEPVVVONE1iMOgreW+5ZRI33HCh7+2/esGY0+nCYjGfNKa/7vuuw2Y7o0EzgxCT8bicO8Em3K/4068787n4jC6c1SOO343tyRs/7eSiQccKDll7xrNoS673mbZMfi+lABSKU5zhKbEArN5ztMULxDgcm8nIeMhvEH/C58ytHvzHj09n5sxbsFprljn0XzBWHdlTWwE4HBv9ZgiDat33Ab/7PnVSJWA0iBYtO9oUkjuE89xVZ/Dswu3086tjXFzhJiHSQv+kaF/bmT3i+Neq/ezMK/XVEgg0SgEoFKc41aaf/LKWj3k/VgGsehBf73PmVg/OdQ3+cCyyp/q82pE9+kziXr9B/nnfdez2dbXuu65Bs4BgO8br4rLhyVw2vGbK5+hQE8WVNf0Vw7rFArBmb6FSAAqFom5+2XEE0KuWtTTVq3SPDeJDvBE7L/g5c+sucF69YMzfB+BP3TOEQd77nlHrvmfUdYsa5JdWBdUv0hhiwy043RrlTjfhFn1Y7tUxksgQE2v3FXJlereTXKFpKAWgUJzibM4pJtRsICkm8PZ/h2M9dvtKbLZ0rNbqwf6JGj4AAKt1UJ0D//FO34H12v1PNEPQ7/tUo3wA2UfK6Nkxomk/vJWJDtOH4h25pZzhffM3GARDkmNYt7+wxe6rFIBCcYozqGsMn6zYh33bYS4aFDgfgMOxnoyM3/uZZF73KYGGDMC60/c+v/7PndDpa7UOIjPz+Tp9APrxhoeFSinZeaS0xd6cA03X2DAAiipcNdqHdotlzs87qXR5CDUb6+raLJQCUChOYTRN8po9m+7x4Vh7BtYEZLev9DPJuLHbV2K1DvEddzg2YLevwmYb4Qvl1J24uinoeLv98U7f2tQ3k2gsOw6XUub0+EIt2zoJ3vUbFbWilgZ3jcGtSXbkljI4OSbg91UKQKE4hfFISWG5k/P6J/pWmQYKmy3da5Jxe/P2p/uO6Xl87vJ7u38FEGRk3ONrmz37nhM6fVuSd5bsRggY1Tu+1e7ZHKrXB7g9NZ3WSd6ZwcGiCqUAFApFTcxGAxcNSmLBpkO4PBpmY+Cyu1itQ8jMfL2GD6Aau31VrdnBKsBY440/P7+IzMzn6nX6thSbc4r5ePlebrB2J7lDeKvcszm4PRr/+EYvizmgS80ZS2p8OGajYNmuAi4Y2Lmu7s1CKQCF4hQnJS6c0io3FS5PQBUA4LX5Dzmu3WYbUWt2MAIQtd74h53Q6dtSpMSH0ykqhKXZ+ZRUuogKDezMKNAcLqli+S499XOPhJpO69hwC306RbE7r6xF7q2SwSkUpzhdO+hmgucWbm+xezgc65g1620cjupC74PJzHyFxx67nczMV7wpHQaRmfkCjz02jczMFwJiy28KkSEmZl8zlF15Zfzh4zVtbjVwbZ5esA2Ae8en1Xk8LsJCQQvVNRBtcaFEfaSnp8uVK1cGWwyFos0xalYmh4orWfnI+TUSjgUCh2NdHdFAJ4/DDzYf/LqHR/67kQ7hZqYM7cpfLx5QbynGYLB+fyFPL9jGLzvyGNw1hi/uGl1nyoc7PljFjsOlfH/f2CbfSwixSkqZXrtdzQAUitOARycPwCAEGc/a+XTF3npLDjaFuu39gcfhgFmz9O9A8Nuzu/PeLWdxtNzFu0t388GyvYG5cDPYV1DOcwu3MeXlxVzy8hI2HijizxP78/kd1nrz/USFmiipdNV5rLk02AcghDACK4EDUsrJQogrgZlAf+AsKeVxr+ZCiL7Ap35NPYG/SClnCyFmAtOAI95jD0spv2nSr1Ao2jkTBifxdcdIHvnvBh789wb+tXI/j08dRL/OzQ+DrNve3zTqS+jmcEBGBjidYLFAZiZYrc0WnbFpHfn3HVZufHsFM7/cRNfYUM7rl9j8CzcATZNoUlLl1vjv2gP8Z/UBX4Ga+AgL08f34dZzepzURxEVaqaksmXSWjfGCXwPsAWo/ovaCFwGvFFfBynlNmAo+BTIAWC+3ynPSymfaYQMCoWiHvp2juLT2618vno/s77ZwqQXF3PbOT2YPj6NMEvTFxFZrWd4o4GqY/6bZv45UUI3u10f/D0e/dtuD4wCABjRPQ7HjPOY8vISbnl3JSO6d+DGUalMGpyEMYBZNp1ujZJKF4s25/Lxin2s21dY43hSTCj3X5DGhQM706cROf6jQk2UOz14NBlQeaGBCkAIkQxMAv4B3AcgpdziPdbQe2UA2VLKPY0XU6FQNASDQXBVejfO75/IE99u5Y2fd7LlUAnv3nRms1IKW61nNNvuf6KEbjab/uZfPQOw2Zp1q+OICjXznztH8eGyvTy9YBur9hzlDx+v4a5xvZgytCt9OkU22T+QX1rFe449zHPsprD8mKnG1rcjA5KicWuSULORezL6NGkAr54hlFa6A77Wo6EzgNnAA0BzUtJdA3xcq+1uIcQN6KalP0opjyvgKYS4HbgdICUlpRm3VyjaDx0iLDx5xRAGdY3m0S828fmq/Vx1ZnDTIpwooZvVqpt97HZ98A/U278/seEW7hrXm2ljevL9llz+9K91vPJjNq/8mE2nqBAGdY2hT2Ik3TqEExdhITLERFSo/gkxGUmMDsViquk2/feq/Tz6xUbKnR7GpnUkvXsHxvbtyOCuMQFzOEeF6MN0caUr4ArgpFFAQojJwEQp5Z1CCBtwv5Ryst9xu7et3vAcIYQFyAEGSilzvW2JQB4ggceAJCnlLSeSRUUBKRSNQ9MkV77hYFdeGT/8cWzQs2M2tqhLS7OvoJyl2Xksycpn26ESduaV4vLUPSYmRIbw9ykDmeitufDkd1t5zZ7NyB5x/GPqYHp3imwRGb/dcJA7PlzNN38Yc9xCsYZSXxRQQ2YAo4FLhBATgVAgWgjxgZTyt424/wRgdfXgD+C/LYSYC3zViOspFIoGYDAIHpsyiMkv/cLTC7bxj6ktU36xoTS3zi/oK2dNAVrw1i0unKvjUrj6TN264NEkR0qqKKxwUlrppqTSTXGliwqnh49X7OPOD1dz/wVplFS6eePnnXSJCeWjaWcH3Dbvz8GiSoAWiQQ6qQKQUs4AZgD4zQAaM/gD/IZa5h8hRJKU8qB3dyq6U1mhUASYAV2iuXFUKu8u3c1V6d186YZPRf675gCP/Hcjsy4bzMVndDl5h0ZiNAg6x4TSuY7U2pePSGbiC7/wjN+Cuy/uPqdFB3+ADQeKAEiICgn4tZusRoUQU4UQ+wEr8LUQYoG3vYsQ4hu/88KB84H/1LrEU0KIDUKI9cA44N6myqJQKE7MveenkRAZwqNfbKxRmPxUYuOBIu79bC2lVW7+7+M1/HfNgVa9v9loYM4N6QztFstlw7qy4x8T6NgCg3JtLEYDidEh9OoYeBNToxSAlNJebf+XUs6XUiZLKUOklIlSygu97TlSyol+fcqllPFSyqJa17peSjlYSjlESnmJ32xAoVAEmOhQM49M6s/6/UV8vDz4C6IaS2G5kzs+XEVMmJnx/fU4/mcXbWt1OXokRPDfu0bz3NVDA553qT7cmsTQQiuY1UpghaKdcMkZXbD2jOfpBdvIL60KtjgNprDcydC/L2JfQQWF5S6+35JLl5hQHp0UfCdya3CktMpXLyDQKAWgULQThBD8fcpAyqrcPPHt1mCL02C+3nC8cWDJQ+e1SHrktsjh4koSo5UCUCgUzaRPYhS3junBv1bt5/q3lvHZin3BFsmH062xq460x99sOEjPhAiGp8T62hw781tRspajyu1hR24Jc37O5qXMHRwtq5n1U0pJbnElHaMCX+8ZVD0AhaLdMT1DTzv8xk87+WVHHm/8nM05vRMY3TsBW99Oxy12ai0+WbGXv3yxid+c1Y1HJw8g3GIir7QKR3Y+d9p6c2V6MmOftgOwYOMhzkyNazU7fCDYm1/OtxsPUu70sGZfIR5NY0lWTUX25bocPpp2ts+5vD23lKPlLvp1bs4a3PpRCkChaGeEWYzMmNCf353bi/+s3s8vO/L4bOV+3nPs4YGL+nKnrXdQ5Mo+XArAJyv2sWxXAS9cPYy1+wvRJL7FV2FmIxUuD+859vD5qv1s/NuFbSrFczVlVW4keuTSzC83seNw6XHRV4nRIVwxIpmoUBM3jUrlQGEFt767kkkv/sLzVw9ldO8E3nPsJtxibJGQV1AKQKFot8RFWLhtTE9uG9OTSpeHfo9+h9OtBU2evDInPRMiePzSQdz32TqmvrqEDhEW0hIj6Z8UhRCCO2y9eG6RHodf5mw7hV48muTxrzez5WAxBworyCmsPG7Af3hiP0Z078DALjEs31WAtVd8jRlM9/gIPpw2ksteXcpTC7Yxv2c8izbnMq5vp4DXeKhGKQCFQoHmTQkTam561tCm4tEkX6w9wM/bjnBmjzhG9U7gu+ljeHj+Br7ZcIhpY3oghKCgzMnz3+uD/8VndGmxAi9SSjblFOPWJAOSon0msaIKFxEWY41VyB5N8tGyPby1eBe788sBmDK0C5cODSfEZKB3p0hG904gwmKqkYzv3LSOdd67f+doTAbBsG6xrNtfyJGSKs4f0HLpq5UCUCgUVLr0N//QANr/pZS8vWQ3XWNDuXBg5+MG6yq3h/mrD/DGzzvZlVfGwC7RzJjQD9ATt71y7XA25RTTP0nPf6NJSYTFRKjZyNNXDGkRZXW4pJJb313pW33bu1Mkf57Un7s+XE2500NsuJm3bzqTYd1i+XbjIWZ+uYnDJVX07BjBy9cO46KBnZuVpmJpdh5uTTK+fyIrdut1gs/pkxCQ31YXSgEoFAqW79KdkZ2iAxNt8p/V+/l81X6WZuvXvXJEMtlHSsktruKD20bSIyGCq153sG5/EYO6RvPadcO5cGDnGm/JQggGdY3x7WtSUlrlprTKzcLNuVzSAnbx1+zZbDlYzD+nDibMYuDh/2zk5ndW+I4Xlrt4a/EuXG6NhZtzSUuM5P4L+nJlenJAZiOZWw8TYTFyVo84Pl+1j87RoS22BgCUAlAoFMCyXQWEmY3NNjcUlbv4aPlenvxOX2dw+fBk9uSX8dP2Ixwu0RefPbNgG/+X0Zu8UiehZgP/u/ucBg2e2w6VAHDBgERGdO/QLDlr49Ek/1uXw2cr9nHhwM5cO1JPDtcjIZINB4rIyi1BAp+u2EdWbinbcku409aL+85PC1hiOiklP2w5zJg+HTEZBMt2FXBGt5iTd2wGSgEoFAoqnB5Mxua/wc7O3M47S3b79u89vw8LN+Xy9682k5YYyfbcUr7ecNC3uGtmI+z41T7V8QMS6Rob1iw5l2Tl8d3GQxworOBwSSU7j5RR7vQQFWLiIa8ZCmBot1iG+iXPW76rgK2HSggxGbh5dI+ADf4Amw8Wc6i4kvP6d2L57gIOFlXWkKUlOHWCaBUKRYtx/oBESirdLNh0qFnXMdYazNfsLfRFsFx7VgpJflk2Z148gJtG92jwtXsmRADw07YjJznzxBwuruS6N5fx/q972FdQTodwCxMGJfHwxH6seGQ83eLC6+372KWDuGV0jxqx+oHihy2HARjXtxNfrM0h3NL8GdnJUDMAhULBuL6d6Bobxv/W5TB5SNNs65UuD/G17NVbDxVzVXo3hICZ/9sMQJ9Okew4XErWkdJGXb9bXDhn94xj66HiJslXzYfL9GR4H00byahejXOwnpkax5mpcc26f31kbj3MGd1iiQkz882Gg1wwIJFwS8sO0UoBKBQKDAZBr06RHCpuWpK47COlnP/cT2hSL35+3/lp/Lwjj0mDu9A9PgJrz3ifQ3iHd8FX97iIRt9HSsg+UkbW4dImVeDaf7Sc137KZkyfBKw94xvdv6XIK61i3f5C7h2fxk/bj1BU4WLKsK4tfl+lABQKBQAdI0N8q3Ebi1EIn43+woGduTK9G1emH6tB/McL+mJYtJ0/ZPThy3UHOFJSxfXW7o2+T4g39HPdvsJGKwBNkzzw+XrMBsGsywa3qRXE9m1HkBLO69eJ13/KJi7Cwjm9Wy78sxqlABQKBQAJURaOlFQhpWz04JiaEEH/pGi2HCzmOm8EjT8junfgg9tGAnBWj6aZUHbnlbF4xxHOTO3AlKGNN1N9sGwPS7PzmXXZYJI71G/nDwY/bM0lMTqE7vHhfL8llytHdGuVPEfKCaxQKAB9BuD0aBRXuJvU/+VrhwF6hE1LsDQ7H03CzEsGnjD6ZueRUg4WVQBQXOli1Z4Cnl24jce/2sK5aR255sxu9fYNBk63xs/b8zivXycWbc6l0qVx6bCWyf1TGzUDUCgUAL6oliOlVcSEmxvdv3plbkGtlMaBwunWc/9UO0ZLq9zszS/nP6v3kxgdypLsPLYdKvEVUa9OHAcgBFw0sDP/nNq2TD8AK3cXUFrlxta3Ex8t20tyhzCGpwR2nUN9KAWgUCgAPwVQUtUkB+viHXp45ks/ZnHP+LSAFktfv7+Qpxdso3t8OAeLKvjb/zZhrxUO2i0ujLN6xNGnUyR5pU525ZVxVo84+iZG0Scxku7xjXc6twaRoSaEgGcXbiPrcCm/H9ur1ZSUUgAKhQLQTUCgzwAayt78cp78bisdo0J4d+luAHp1jESTEiNNH8Syjmbxz+X/JNQYijv3ahZuKCUhMoS7x/XmprdXEBtu5vLhyYzuHU/fzlEkRIaQEBkSUKXTWgxJjuWZK87gr19uYuqwZH43tler3VspAIVCAdScATSUez5dw5q9hb79j6edjbVX08MrnR4nczfM5c0NbxJhjqDMWU5VRTbG8Amc23ckc37eSXKHMObfObpJZqq2yuUjkrlseNdWN0812AkshDAKIdYIIb7y7l8phNgkhNCEEOkn6LdbCLFBCLFWCLHSrz1OCLFICLHD+906Ri+FQlEnMWFmhICi8hPb8PNKq8jcksvP24+wdl8h4/sfW63a1AgfgILKAq7835W8vu51Lky9kC8v/ZJpg3+HMTSH8O5v8U3u8+w4XML089NOq8G/mmD4JhozA7gH2AJEe/c3ApcBbzSg7zgpZe3QgIeATCnlE0KIh7z7DzZCHoVCEUCEEERaTBworGT299t5zZ4N6Au7RvaIZ/r5fdieW8rt81ZS5Vc4ZkCXaELMBkwG0WQTTF5FHr/s/4WdRTt5YswTTOo5CYDbhtyMwSB5Ze0rmKO2cN25CUzyVgdTNJ8GKQAhRDIwCfgHcB+AlHKL91hT7z0FsHm33wPsKAWgUASVUqebf6/eD4DFaODakSnkFlcyf+0BPl2pF5CPsBh54+YzAdiwv4gr07vROaZpaaQ1qXHPD/dg32/3tQ1JGOLbXn9kPZ9s/YQwUxh/G/U3JvQ4u+YFyvLAEgHm5iWHa680dAYwG3gAaEplYgksFEJI4A0p5Rxve6KU8iCAlPKgEKJTXZ2FELcDtwOkpBy/wEShUASOu2y9mb/mAE9cPpjU+AhfYrRlO/O5es6vAPz4JxudovQB39a3zv+2DWJv8V4eXfIoqw+vBuDGATeSFpdGclQybs3NOxvf4ZW1r5AclcybF7xJ7w61ahXvtMPH10JkJ7j8LUge0WRZ2isnVQBCiMnAYSnlKiGErQn3GC2lzPEO8IuEEFullD83tLNXYcwBSE9Plyc5XaFQNIP7L+zL/Rf2Pa49NeFYCGWH8ObVp9WkxkdbPuKF1S9gNpr5xzn/4OKeF/usCdsKtvGXpX9hc/5mLky9kL9a/0qUpda75+Yv4d+3QlxPcJbBW+fD0Gth0GXQ67xmydeeaMgMYDRwiRBiIhAKRAshPpBS/rYhN5BS5ni/Dwsh5gNnAT8DuUKIJO/bfxJwuGk/QaFQtDQdwi2M69uRhMiQZqUo8H/rPzf5XP5y9l9IjDjmRHbkOLgz806iLdE8O/ZZLki9oOYFKothxVz44XHomg7XfgrCAAsfgU3zYc37cO6fYNyf9dVfihMipGz4S7V3BnC/lHKyX5vd27ayjvMjAIOUssS7vQj4u5TyOyHE00C+nxM4Tkr5wInun56eLleuPO42CoWiDZFXocd7JIQdS2ZW463fYObBsx7kkl6X1PAhFlUVcdkXlxFpieTdi96lQ2itwMCfnoalL0JVMfSdCJe/qdv/q3FXwdd/1JXADV9Cz7Et+jtPJYQQq6SUx0VrNlmVCyGmCiH2A1bgayHEAm97FyHEN97TEoHFQoh1wHLgaynld95jTwDnCyF2AOd79xUKxSmMI8fBhH9P4PIvL2dfse403lu8l5u/u5knVzzJWUlnMX/KfKb0nlJj8D9Udog7vr+DgsoCZo2Zdfzgv/Jt+PFxSB0D036A33xcc/AHMIXAhf/Qt/evQHFyGrUQTEppR4/WQUo5H5hfxzk5wETv9k7gjHqulQ9kNEpahULRpvlx349Ueipxa25uX3Q7l6ddzhvr3sBsNPP46MePe+uXUrL4wGIeXvwwLs3FM2OfYUD8gGMXXPsR/PoaHFqv2/avfh8MxvoFCI2BqC5QsLMFf+Xpg1oJrFAoAsbh8sOkRKUwa8wsblt4Gy+sfoGxyWP5i/UvdArXI4bKXeVsyt/EhrwNfLfrO7YUbKF3bG+etz1PakzqsYvlrIX/3gGJg2DiMzD0uhMP/tWExkBlUYv8vtMNpQAUCkVAcOQ4sO+zc0HqBQzpOIQ3L3iTIxVHOK/beb63/qyjWdy68FYKKgsA6NuhL4+MfIRLel9CmKlWLP+aD8AYAjd9BWGNSBQQGq37CWrjqoR/3Qjl+dB9FGTMBEP7zoivFIBCoWg2BZUFzPhlBj1ievBX618BGNLx2IKu/Ip8ftj3A6+seQWjMDLbNpvhicOPt/VX46qADZ/BgEsaN/gDhETB7iWQnw3xfonV7LNg+3eQNBSWvAA9zoXe4xv5S08vlAJQKBTN5qU1L1HsLOaVjFfIKc1hV9Eu/VO8i52FO9l2dBua1OgV04vnxj1Hz5ieJ77gmg90M86wBkWb1+SM3+gK4OUzYfCVkH4LhMWC42X9epOeg2f7wfrPlAIItgAKheLUxyT0oeTmBTdT4a7wtSdFJNEzpifTBk/j/O7nk9Yh7eTpY3Ysgu8e0t/QU89tvDCDr9CjhZa+qEcPrf9Ebw+JhvF/06OFIhLAXdn4a59mKAWgUCiazZV9r+TzHZ+TnpjOxJ4T6RnTk9ToVMLNjay9e2A1fHYDdBoAV3/YdBt9VKIeEmp7SF81vGk+DLlKH/gB8rZDRMemXTtAlK9ciSU1FVNCyxd/r49GLQQLNmohmELRdilxlhBpjmx6gkiPC944V1/te/uPeo6flmJmjPc7ONFC7qNH2WEdBUCPL74gtG9ai94v4AvBFAqFwp8oS1TzctovewMOb4YJT7bs4F9NEO3/xshjJTd3TZmCu6AgKHIoBaBQKIJPcY4epdPnAug3qWXvVW316Bq87KHCbKb7Rx/59ndeMoVgWGOUAlAoFK2LqxJ2/azb5Xfa9QVf3z6om4AmPNnySdyqnb9BriEQPnwYaSuWA+DJy6P0hx9aXQblBFYoFK2D5tEdvDsWgaeOusO2GXp655bG5Y1SaqyDugUwREZi7NABz9GjHHnlFSLPO69VS0MqBaBQKFqHI9tg61cwcCoMuQZiu0FFIVQW6pk8+1/cuOtJqb/NS6mnhDYYAaFvC1H/TMJVrn+bGlbFTErZYoNy1Y4deI4eJWz4cCpWr6bs55+JHNt6WUyVAlAoFK1DSY7+3X009L2oEf1yYd+vsPdX2LccSg+Ds0SPFpKek3QWuqnHFKq/8ZvDdAUBx2cTrYPy1avZ97vfIz0eQvv0ocuzz2JJ7tpw2U9C6Y92ALo89RR7b7iBvFdfI+Lcc1ttFqAUgEKhaB16jNUXdy14GDoPhpSz6z6v+CBs/xb2LtMH/qO79XZTqO64TR0Nlkg9548lUh/QpQc0DZD6jEBq+gepm3zclfq3q1z3QcT1hG4jTypyxZq1aCUlRJ53HuXLlpHzwAN0f38ewtiApHQnwVNaxtEPPiA8PR1Lclfib5/GoZl/o3zZMiLOrufZBBilABQKRetgNMOV78Gb4+GT6/RY/9hadb6zvofPb9HTQER01AfpM2+DFCt0HgKm5pWjbCwR1rPBaMSVk0PsFVdQ8N57FMx7n/ibb2rWdaWU5D72GO68PLq++AIAMVOnkvfqa+S99nqrKQAVBaRQKFqP8Di9jKPmgo+uhqqSY8dWvAkfXgkx3eCOpXD/DrjmQxj1f5Cc3uqDP0DogAEkv/wS7vw8Cj74AIDDTz7JzssuQ3M6m3RN6fFw5NlnKfriCxLuvJPwYcMAMISEEHfLzZQvW0b56jUB+w0nQikAhULRusT31uP9D2+GHQv1Ns0Di2ZC6jlwywJIHNhmavpGjRtHr6++InLMGF9b1eYt7Lp0Kp6SkhP0rImUEue+fey9+Rby33yL2KuuIuHuu2qc0+GqqzB26EDe668FTP4ToUxACoWi9XBVwJf/Bxv+BcOuh/6X6O2H1uuO3WHXQ0jkia8RBIwxMXR5YhZZGeMxd08hatx55L3yCsVffUXU+PHkPPgQrpwcwtJHYLCEgBAIsxmMBrSyMpx79lCxajXS6USEhZH0z38SM/XS45y9hvBw4m66iSPPP0/Fxk2EDRrYor9L5QJSKBStQ3GObvvPWQ0Zf4Fz7jv2lv/RNbD7F7hn3bGEbW2Q0p9+Yt8dd2JOTsa1dy+G8HAixp5Lybff+c4xxMQgAOlyId1uPdY/NpaI0aOxdEsmcuxYLN2713sPT2kpWePOI3LMGLo+92xA5K4vF5CaASgUipan6ADMPQ+cpXDNRzXTPWR9r0f9jP9bmx78ASLHjqXLrH+S98YcALTy8hqDf9fnniV64sRm3cMYGUnsZVMp+PAjEvPyWjRbaIN9AEIIoxBijRDiK+/+lUKITUIITQhxnGbxntNNCPGjEGKL99x7/I7NFEIcEEKs9X6a99QUCkXbxVkKpYfAelfNwd/thG8f0sMyz74jePI1gpgpU+j59Vck17LTm5OTCbdaA3KPiHPPBbcb565dAblefTTGCXwPsMVvfyNwGfDzCfq4gT9KKfsDZwN3CSEG+B1/Xko51Pv5phGyKBSKU4mOfSFlFKz7RHf4VrN8DuTvgAtn6YVaThGEEETZbPRdv464G28AwF1QQOFn/0KrqiPNRWPRtOZfowE0SAEIIZKBScCb1W1Syi1Sym0n6ielPCilXO3dLkFXIIFbRqdQKE4dRt4OhXv0Kl2gr+j96Uk9LXPahcGVrYkYLBYSZ8yg16KFRIyycuT558meMIH8t97GffRok69bsXYdACFpLVsnoKE+gNnAA0BUU28khEgFhgHL/JrvFkLcAKxEnyk0/YkpFIq2zYBLoVcGLHwUep0Hi5/XV+Ze9ESbCflsKpZu3ej28suUORzkvfIqh59+msNPP03okCHEXHwxkePGYYyMoOzXX/EUFWPu0gVLj1TMSUm+VcVSSio3bqLw359T+Pm/CT/zTIwxMS0q90mjgIQQk4GJUso7hRA24H4p5WS/43ZvW73hOUKISOAn4B9Syv942xKBPEACjwFJUspb6uh7O3A7QEpKyog9e/Y05vcpFIq2RHEOvGrVV/nmZ+k+gQv/EWypAk7l1q2U/vgjxQsWUrV1a73nCbMZc/cULCndce3fT9X27YjQUKInTyLxT38KmAKoLwqoIQpgFnA9uj0/FIgG/iOl/K33uJ0TKAAhhBn4ClggpXyunnNSga+klINOJIsKA1UoTgM2fA7/vlVXAv+3CkJb9i032FRlZ1O+YgVaWRmh/ftj6dED14EDOHfvpmrXLpy79+DauwdDdAwxF08metIkjFFNNrbUSZPDQKWUM4AZ3ovY0Af73zbwpgJ4C9hSe/AXQiRJKQ96d6eiO5UVCsXpzuArdPt/58Gn/eAPENKrFyG9etVoMyclEZ5eZ/Bkq9LkVBBCiKlCiP2AFfhaCLHA295FCFEd0TMaffZwXh3hnk8JITYIIdYD44B7m/4zFArFKYX1Tugx5uTnKVoUtRJYoVAoTnPqMwGpZHAKhULRTlEKQKFQKNopSgEoFApFO0UpAIVCoWinKAWgUCgU7RSlABQKhaKdohSAQqFQtFNOqXUAQogjQHOSASWg5x9q65wqcsKpI+upIiecOrKeKnLCqSNrS8nZXUrZsXbjKaUAmosQYmVdiyHaGqeKnHDqyHqqyAmnjqynipxw6sja2nIqE5BCoVC0U5QCUCgUinZKe1MAc4ItQAM5VeSEU0fWU0VOOHVkPVXkhFNH1laVs135ABQKhUJxjPY2A1AoFAqFF6UAFAqFop1y2ikAIcSnfsVndgsh1nrbz/JrXyeEmFpP/5lCiAN1FLBpa3LGCSEWCSF2eL87tIScJ5H1fCHEKm9hn1VCiPPq6R/sZ9pQOVvlmZ5AznghxI9CiFIhxMsn6N8qzzNAsgb1mXqPzRBCZAkhtgkhLqynf9CfaSNkDdwzlVKeth/gWeAv3u1wwOTdTgIOV+/X6jMTvexlW5fzKeAh7/ZDwJNBkHUY0MW7PQg4UE+fYD/ThsrZ6s+0lpwRwDnA74GXT9Cn1Z9nM2QN9jMdAKwDQoAeQDZgbKPPtKGyBuyZnnYzgGqEEAK4CvgYQEpZLqV0ew+HAm3C+90MOacA73m33wMubUExgTplXSOlzPEe3gSECiFCWlqOk9EMOVv1mdYhZ5mUcjFQ2ZL3bQrNkDWoz9R7/0+klFVSyl1AFnBWS8rQUJoha8Ce6WmrAIAxQK6Uckd1gxBipBBiE7AB+L3fQFubu4UQ64UQb7ekaaWZciZKKQ8CeL87tbCcdcrqx+XAGillVT19g/pM/TiRnK39TE8k58lozecJTZc12M+0K7DP7/h+b1tdBPuZNlTWgD3TU1IBCCG+F0JsrOMzxe+033BMswIgpVwmpRwInAnMEEKE1nH514BewFDgIPoUrS3KGVCaKqu370DgSeB39Vw+6M+0gXIGjObI2QAC9jxbQdaA0UQ5RR2XqmtW3RaeaUNlDRytbfNqJbuaCcgFkk9wzo9A+kmukwpsbItyAtuAJO92ErAtGM8USAa2A6MbeJ2gPNOGyNmaz/RE//bATZzArt6az7O5sgb7mQIzgBl++wsAa1t8pg2VNZDP9JScATSA8cBWKeX+6gYhRA8hhMm73R3oC+yu3VEIkeS3OxXY2BblBL4EbvRu3wh80YJyQt2yxgJfo//RLqmvYxt4prE0QE5a95keJ2dDaeXnCc2QleA/0y+Ba4QQIUKIHkAfYHntjm3kmTZIVgL5TFtSywXrA7yLbjv3b7se3QG4FlgNXOp37E28b9nA++i29/XeB53URuWMBzKBHd7vuCA800eAMq+s1Z9ObfCZNlTOVnumdcnpbd8NFACl6DbgAcF8ngGQtS080z+jR9RsAyb4tbfFZ9oQWQP2TFUqCIVCoWinnK4mIIVCoVCcBKUAFAqFop2iFIBCoVC0U5QCUCgUinaKUgAKhULRTlEKQKFQKNopSgEoFApFO+X/AfHzVocvTn6rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "minPlot = 1.3\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "plotPoly(countyMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "45d704ce-17ee-45eb-9355-450aedb8ec53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17904669601612833 = std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"= std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9a12c180-f6b1-4011-bd9e-031cce858e01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12473939677977047 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50b8ba5e-2507-4b7d-b384-aa54828eb217",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.71\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f63bdad6-77c0-4300-8529-b6e11b018be3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by -0.00113 0.32771 HD avg vs true 0.32884\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "ccd7984d-03b4-4f65-b7bf-13b043d171a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  781099.3333333334\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is a plot of red-blue lean by Home District area\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OPTIONAL: VISUALIZE some HOME DISTRICT stats\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()\n",
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()\n",
    "print(\"Here is a plot of red-blue lean by Home District area\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()\n",
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "99050d87-f8f7-4cf3-bd54-4002195e2ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "16dba1fd-a79f-406f-b172-8e7aa4628c82",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "51e4b31b-c36a-48e3-b0c6-abfa3a14dc9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\", state=\"+str(STATE), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "3b4147ed-c1bc-4182-ba89-cbd43454112e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 0.001 0.419\n",
      "fractional expected GOP seats =  0.032  out of  9 seats\n",
      "simpler expected GOP seats =  0.0  out of  9 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats\")\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "852cf759-7016-46f9-bf48-21031836f099",
   "metadata": {},
   "outputs": [],
   "source": [
    "# BACKUP HD1 with alternate approach to wedge angles when there are two shorted wedges  8/9/22 - led to wider tractUse distro\n",
    "date = \"11Aug\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t\n",
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  }
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